############################################################
#
# DESeq2 organization of R files
#
# core ........... most of the statistical code (example call below)
# fitNbinomGLMs .. three functions for fitting NB GLMs
# methods ........ the S4 methods (estimateSizeFactors, etc.)
# AllClasses ..... class definitions and object constructors
# AllGenerics .... the generics defined in DESeq2
# results ........ results() function and helpers
# plots .......... all plotting functions
# lfcShrink ...... log2 fold change shrinkage
# helper ......... unmix, collapseReplicates, fpkm, fpm, DESeqParallel
# expanded ....... helpers for dealing with expanded model matrices
# wrappers ....... the R wrappers for the C++ functions (mine)
# RcppExports .... the R wrappers for the C++ functions (auto)
#
# rlogTransformation ... rlog
# varianceStabilizingTransformation ... VST
#
# general outline of the internal function calls.
# note: not all of these functions are exported.
#
# DESeq
# |- estimateSizeFactors
#    |- estimateSizeFactorsForMatrix
# |- estimateDispersions
#    |- estimateDispersionsGeneEst
#       |- fitNbinomGLMs
#          |- fitBeta (C++)
#       |- fitDisp (C++)
#    |- estimateDispersionsFit
#    |- estimateDispersionsMAP
#       |- estimateDispersionPriorVar
#       |- fitDisp (C++)
# |- nbinomWaldTest
#    |- fitGLMsWithPrior
#       |- fitNbinomGLMs
#          |- fitBeta (C++)
#       |- estimateBetaPriorVar
#       |- fitNbinomGLMs
#          |- fitBeta (C++)
#
############################################################


#' DESeq2 package for differential analysis of count data
#' 
#' The DESeq2 package is designed for normalization,
#' visualization, and differential analysis of high-dimensional
#' count data. It makes use of empirical Bayes techniques
#' to estimate priors for log fold change and dispersion, and
#' to calculate posterior estimates for these quantities.
#'
#' The main functions are:
#'
#' \itemize{
#' \item \code{\link{DESeqDataSet}} - build the dataset, see tximeta & tximport packages for preparing input
#' \item \code{\link{DESeq}} - perform differential analysis
#' \item \code{\link{results}} - build a results table
#' \item \code{\link{lfcShrink}} - estimate shrunken LFC (posterior estimates) using apeglm & ashr pakges
#' \item \code{\link{vst}} - apply variance stabilizing transformation, e.g. for PCA or sample clustering
#' \item Plots, e.g.: \code{\link{plotPCA}}, \code{\link{plotMA}}, \code{\link{plotCounts}}
#' }
#' 
#' For detailed information on usage, see the package vignette, by typing
#' \code{vignette("DESeq2")}, or the workflow linked to on the first page
#' of the vignette.
#' 
#' All software-related questions should be posted to the Bioconductor Support Site:
#' 
#' \url{https://support.bioconductor.org}
#'
#' The code can be viewed at the GitHub repository,
#' which also lists the contributor code of conduct:
#'
#' \url{https://github.com/mikelove/tximport}
#' 
#' @references
#'
#' Love, M.I., Huber, W., Anders, S. (2014)
#' Moderated estimation of fold change and dispersion
#' for RNA-seq data with DESeq2. Genome Biology, 15:550.
#' \url{https://doi.org/10.1186/s13059-014-0550-8}
#'
#' @author Michael Love, Wolfgang Huber, Simon Anders
#' 
#' @docType package
#' @name DESeq2-package
#' @aliases DESeq2-package
#' @keywords package
NULL

#' Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution
#'
#' This function performs a default analysis through the steps:
#' \enumerate{
#' \item estimation of size factors: \code{\link{estimateSizeFactors}}
#' \item estimation of dispersion: \code{\link{estimateDispersions}}
#' \item Negative Binomial GLM fitting and Wald statistics: \code{\link{nbinomWaldTest}}
#' }
#' For complete details on each step, see the manual pages of the respective
#' functions. After the \code{DESeq} function returns a DESeqDataSet object,
#' results tables (log2 fold changes and p-values) can be generated
#' using the \code{\link{results}} function.
#' Shrunken LFC can then be generated using the \code{\link{lfcShrink}} function. 
#' All support questions should be posted to the Bioconductor
#' support site: \url{http://support.bioconductor.org}.
#'
#' The differential expression analysis uses a generalized linear model of the form:
#'
#' \deqn{ K_{ij} \sim \textrm{NB}( \mu_{ij}, \alpha_i) }{ K_ij ~ NB(mu_ij, alpha_i) }
#' \deqn{ \mu_{ij} = s_j q_{ij} }{ mu_ij = s_j q_ij }
#' \deqn{ \log_2(q_{ij}) = x_{j.} \beta_i }{ log2(q_ij) = x_j. beta_i }
#'
#' where counts \eqn{K_{ij}}{K_ij} for gene i, sample j are modeled using
#' a Negative Binomial distribution with fitted mean \eqn{\mu_{ij}}{mu_ij}
#' and a gene-specific dispersion parameter \eqn{\alpha_i}{alpha_i}.
#' The fitted mean is composed of a sample-specific size factor
#' \eqn{s_j}{s_j} and a parameter \eqn{q_{ij}}{q_ij} proportional to the
#' expected true concentration of fragments for sample j.
#' The coefficients \eqn{\beta_i}{beta_i} give the log2 fold changes for gene i for each
#' column of the model matrix \eqn{X}{X}.
#' The sample-specific size factors can be replaced by
#' gene-specific normalization factors for each sample using
#' \code{\link{normalizationFactors}}.
#'
#' For details on the fitting of the log2 fold changes and calculation of p-values,
#' see \code{\link{nbinomWaldTest}} if using \code{test="Wald"},
#' or \code{\link{nbinomLRT}} if using \code{test="LRT"}.
#'
#' Experiments without replicates do not allow for estimation of the dispersion
#' of counts around the expected value for each group, which is critical for
#' differential expression analysis. Analysis without replicates was deprecated
#' in v1.20 and is no longer supported since v1.22.
#' 
#' The argument \code{minReplicatesForReplace} is used to decide which samples
#' are eligible for automatic replacement in the case of extreme Cook's distance.
#' By default, \code{DESeq} will replace outliers if the Cook's distance is
#' large for a sample which has 7 or more replicates (including itself).
#' This replacement is performed by the \code{\link{replaceOutliers}}
#' function. This default behavior helps to prevent filtering genes
#' based on Cook's distance when there are many degrees of freedom.
#' See \code{\link{results}} for more information about filtering using
#' Cook's distance, and the 'Dealing with outliers' section of the vignette.
#' Unlike the behavior of \code{\link{replaceOutliers}}, here original counts are
#' kept in the matrix returned by \code{\link{counts}}, original Cook's
#' distances are kept in \code{assays(dds)[["cooks"]]}, and the replacement
#' counts used for fitting are kept in \code{assays(dds)[["replaceCounts"]]}.
#'
#' Note that if a log2 fold change prior is used (betaPrior=TRUE)
#' then expanded model matrices will be used in fitting. These are
#' described in \code{\link{nbinomWaldTest}} and in the vignette. The
#' \code{contrast} argument of \code{\link{results}} should be used for
#' generating results tables.
#' 
#' @return a \code{\link{DESeqDataSet}} object with results stored as
#' metadata columns. These results should accessed by calling the \code{\link{results}}
#' function. By default this will return the log2 fold changes and p-values for the last
#' variable in the design formula.  See \code{\link{results}} for how to access results
#' for other variables.
#'
#' @param object a DESeqDataSet object, see the constructor functions
#' \code{\link{DESeqDataSet}},
#' \code{\link{DESeqDataSetFromMatrix}},
#' \code{\link{DESeqDataSetFromHTSeqCount}}.
#' @param test either "Wald" or "LRT", which will then use either 
#' Wald significance tests (defined by \code{\link{nbinomWaldTest}}),
#' or the likelihood ratio test on the difference in deviance between a
#' full and reduced model formula (defined by \code{\link{nbinomLRT}})
#' @param fitType either "parametric", "local", "mean", or "glmGamPoi"
#' for the type of fitting of dispersions to the mean intensity.
#' See \code{\link{estimateDispersions}} for description.
#' @param sfType either "ratio", "poscounts", or "iterate"
#' for the type of size factor estimation. See
#' \code{\link{estimateSizeFactors}} for description. 
#' @param betaPrior whether or not to put a zero-mean normal prior on
#' the non-intercept coefficients 
#' See \code{\link{nbinomWaldTest}} for description of the calculation
#' of the beta prior. In versions \code{>=1.16}, the default is set
#' to \code{FALSE}, and shrunken LFCs are obtained afterwards using
#' \code{\link{lfcShrink}}.
#' @param full for \code{test="LRT"}, the full model formula,
#' which is restricted to the formula in \code{design(object)}.
#' alternatively, it can be a model matrix constructed by the user.
#' advanced use: specifying a model matrix for full and \code{test="Wald"}
#' is possible if \code{betaPrior=FALSE}
#' @param reduced for \code{test="LRT"}, a reduced formula to compare against,
#' i.e., the full formula with the term(s) of interest removed.
#' alternatively, it can be a model matrix constructed by the user
#' @param quiet whether to print messages at each step
#' @param minReplicatesForReplace the minimum number of replicates required
#' in order to use \code{\link{replaceOutliers}} on a
#' sample. If there are samples with so many replicates, the model will
#' be refit after these replacing outliers, flagged by Cook's distance.
#' Set to \code{Inf} in order to never replace outliers.
#' @param modelMatrixType either "standard" or "expanded", which describe
#' how the model matrix, X of the GLM formula is formed.
#' "standard" is as created by \code{model.matrix} using the
#' design formula. "expanded" includes an indicator variable for each
#' level of factors in addition to an intercept. for more information
#' see the Description of \code{\link{nbinomWaldTest}}.
#' betaPrior must be set to TRUE in order for expanded model matrices
#' to be fit.
#' @param useT logical, passed to \code{\link{nbinomWaldTest}}, default is FALSE,
#' where Wald statistics are assumed to follow a standard Normal
#' @param minmu lower bound on the estimated count for fitting gene-wise dispersion
#' and for use with \code{nbinomWaldTest} and \code{nbinomLRT}.
#' If \code{fitType="glmGamPoi"}, then 1e-6 will be used
#' (as this fitType is optimized for single cell data, where a lower
#' minmu is recommended), otherwise the default value
#' as evaluated on bulk datasets is 0.5
#' @param parallel if FALSE, no parallelization. if TRUE, parallel
#' execution using \code{BiocParallel}, see next argument \code{BPPARAM}.
#' A note on running in parallel using \code{BiocParallel}: it may be
#' advantageous to remove large, unneeded objects from your current
#' R environment before calling \code{DESeq},
#' as it is possible that R's internal garbage collection
#' will copy these files while running on worker nodes.
#' @param BPPARAM an optional parameter object passed internally
#' to \code{\link{bplapply}} when \code{parallel=TRUE}.
#' If not specified, the parameters last registered with
#' \code{\link{register}} will be used.
#' 
#' @author Michael Love
#' 
#' @references
#'
#' Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. \url{https://doi.org/10.1186/s13059-014-0550-8}
#'
#' For \code{fitType="glmGamPoi"}:
#' 
#' Ahlmann-Eltze, C., Huber, W. (2020) glmGamPoi: Fitting Gamma-Poisson Generalized Linear Models on Single Cell Count Data. Bioinformatics. \url{https://doi.org/10.1093/bioinformatics/btaa1009}
#' 
#' @import BiocGenerics BiocParallel S4Vectors IRanges GenomicRanges SummarizedExperiment Biobase Rcpp methods
#'
#' @importFrom locfit locfit
#' @importFrom genefilter rowVars filtered_p
#' @importFrom graphics axis hist plot points
#' @importFrom stats Gamma as.formula coefficients df dnbinom dnorm formula glm loess lowess model.matrix optim p.adjust pchisq pnorm prcomp predict pt qf qnorm rchisq relevel rnbinom rnorm runif splinefun terms terms.formula approx
#' @importFrom utils read.table read.csv askYesNo menu
#' @importFrom stats4 summary
#' 
#' @useDynLib DESeq2
#'
#' @seealso \code{link{results}}, \code{\link{lfcShrink}}, \code{\link{nbinomWaldTest}}, \code{\link{nbinomLRT}}
#'
#' @examples
#'
#' # see vignette for suggestions on generating
#' # count tables from RNA-Seq data
#' cnts <- matrix(rnbinom(n=1000, mu=100, size=1/0.5), ncol=10)
#' cond <- factor(rep(1:2, each=5))
#'
#' # object construction
#' dds <- DESeqDataSetFromMatrix(cnts, DataFrame(cond), ~ cond)
#'
#' # standard analysis
#' dds <- DESeq(dds)
#' res <- results(dds)
#'
#' # moderated log2 fold changes
#' resultsNames(dds)
#' resLFC <- lfcShrink(dds, coef=2, type="apeglm")
#' 
#' # an alternate analysis: likelihood ratio test
#' ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1)
#' resLRT <- results(ddsLRT)
#'
#' @export
DESeq <- function(object, test=c("Wald","LRT"),
                  fitType=c("parametric","local","mean", "glmGamPoi"),
                  sfType=c("ratio","poscounts","iterate"),
                  betaPrior,
                  full=design(object), reduced, quiet=FALSE,
                  minReplicatesForReplace=7, modelMatrixType,
                  useT=FALSE, minmu=if (fitType=="glmGamPoi") 1e-6 else 0.5,
                  parallel=FALSE, BPPARAM=bpparam()) {
  # check arguments
  stopifnot(is(object, "DESeqDataSet"))
  test <- match.arg(test, choices=c("Wald","LRT"))
  fitType <- match.arg(fitType, choices=c("parametric","local","mean","glmGamPoi"))
  dispersionEstimator <- if (fitType == "glmGamPoi") {
    "glmGamPoi"
  } else {
    "DESeq2"
  }
  sfType <- match.arg(sfType, choices=c("ratio","poscounts","iterate"))
  # more check arguments
  stopifnot(is.logical(quiet))
  stopifnot(is.numeric(minReplicatesForReplace))
  stopifnot(is.logical(parallel))
  modelAsFormula <- !is.matrix(full) & is(design(object), "formula")

  if (missing(betaPrior)) {
    betaPrior <- FALSE
  } else {
    stopifnot(is.logical(betaPrior))
  }
  # get rid of any NA in the mcols(mcols(object))
  object <- sanitizeRowRanges(object)
  
  if (test == "LRT") {
    if (missing(reduced)) {
      stop("likelihood ratio test requires a 'reduced' design, see ?DESeq")
    }
    if (betaPrior) {
      stop("test='LRT' does not support use of LFC shrinkage, use betaPrior=FALSE")
    }
    if (!missing(modelMatrixType) && modelMatrixType=="expanded") {
      stop("test='LRT' does not support use of expanded model matrix")
    }
    if (is.matrix(full) | is.matrix(reduced)) {
      if (!(is.matrix(full) & is.matrix(reduced))) {
        stop("if one of 'full' and 'reduced' is a matrix, the other must be also a matrix")
      }
    }
    if (modelAsFormula) {
      checkLRT(full, reduced)
    } else {
      checkFullRank(full)
      checkFullRank(reduced)
      if (ncol(full) <= ncol(reduced)) {
        stop("the number of columns of 'full' should be more than the number of columns of 'reduced'")
      }
    }
  }
  if (test == "Wald" & !missing(reduced)) {
    stop("'reduced' ignored when test='Wald'")
  }
  if (dispersionEstimator == "glmGamPoi" && test == "Wald") {
    warning("glmGamPoi dispersion estimator should be used in combination with a LRT and not a Wald test.",
            call. = FALSE)
  }
  
  if (modelAsFormula) {
    # run some tests common to DESeq, nbinomWaldTest, nbinomLRT
    designAndArgChecker(object, betaPrior)

    if (design(object) == formula(~1)) {
      warning("the design is ~ 1 (just an intercept). is this intended?")
    }
    
    if (full != design(object)) {
      stop("'full' specified as formula should equal design(object)")
    }
    modelMatrix <- NULL
  } else {
    # model not as formula, so DESeq() is using supplied model matrix
    if (!quiet) message("using supplied model matrix")
    if (betaPrior == TRUE) {
      stop("betaPrior=TRUE is not supported for user-provided model matrices")
    }
    checkFullRank(full)
    # this will be used for dispersion estimation and testing
    modelMatrix <- full
  }
 
  attr(object, "betaPrior") <- betaPrior
  stopifnot(length(parallel) == 1 & is.logical(parallel))
  
  if (!is.null(sizeFactors(object)) || !is.null(normalizationFactors(object))) {
    if (!quiet) {
      if (!is.null(normalizationFactors(object))) {
        message("using pre-existing normalization factors")
      } else {
        message("using pre-existing size factors")
      }
    }
  } else {
    if (!quiet) message("estimating size factors")
    object <- estimateSizeFactors(object, type=sfType, quiet=quiet)
  }
  
  if (!parallel) {
    if (!quiet) message("estimating dispersions")
    object <- estimateDispersions(object, fitType=fitType, quiet=quiet, modelMatrix=modelMatrix, minmu=minmu)
    if (!quiet) message("fitting model and testing")
    if (test == "Wald") {
      object <- nbinomWaldTest(object, betaPrior=betaPrior, quiet=quiet,
                               modelMatrix=modelMatrix,
                               modelMatrixType=modelMatrixType,
                               useT=useT,
                               minmu=minmu)
    } else if (test == "LRT") {
      object <- nbinomLRT(object, full=full,
                          reduced=reduced, quiet=quiet,
                          minmu=minmu,
                          type = dispersionEstimator)
    }
  } else if (parallel) {
    if (!missing(modelMatrixType)) {
      if (betaPrior) stopifnot(modelMatrixType=="expanded")
    }
    object <- DESeqParallel(object, test=test, fitType=fitType,
                            betaPrior=betaPrior, full=full, reduced=reduced,
                            quiet=quiet, modelMatrix=modelMatrix,
                            useT=useT, minmu=minmu,
                            BPPARAM=BPPARAM)
  }

  # if there are sufficient replicates, then pass through to refitting function
  sufficientReps <- any(nOrMoreInCell(attr(object,"modelMatrix"),minReplicatesForReplace))
  if (sufficientReps) {
    object <- refitWithoutOutliers(object, test=test, betaPrior=betaPrior,
                                   full=full, reduced=reduced, quiet=quiet,
                                   minReplicatesForReplace=minReplicatesForReplace,
                                   modelMatrix=modelMatrix,
                                   modelMatrixType=modelMatrixType)
  }

  # stash the package version (again, also in construction)
  metadata(object)[["version"]] <- packageVersion("DESeq2")
  
  object
}

#' Make a simulated DESeqDataSet
#'
#' Constructs a simulated dataset of Negative Binomial data from
#' two conditions. By default, there are no fold changes between
#' the two conditions, but this can be adjusted with the \code{betaSD} argument.
#'
#' @param n number of rows
#' @param m number of columns
#' @param betaSD the standard deviation for non-intercept betas, i.e. beta ~ N(0,betaSD)
#' @param interceptMean the mean of the intercept betas (log2 scale)
#' @param interceptSD the standard deviation of the intercept betas (log2 scale)
#' @param dispMeanRel a function specifying the relationship of the dispersions on
#' \code{2^trueIntercept}
#' @param sizeFactors multiplicative factors for each sample
#'
#' @return a \code{\link{DESeqDataSet}} with true dispersion,
#' intercept and beta values in the metadata columns.  Note that the true
#' betas are provided on the log2 scale.
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' dds
#'
#' @export
makeExampleDESeqDataSet <- function(n=1000,m=12,betaSD=0,interceptMean=4,interceptSD=2,
                                    dispMeanRel=function(x) 4/x + .1,sizeFactors=rep(1,m)) {
  beta <- cbind(rnorm(n,interceptMean,interceptSD),rnorm(n,0,betaSD))
  dispersion <- dispMeanRel(2^(beta[,1]))
  colData <- DataFrame(condition=factor(rep(c("A","B"),times=c(ceiling(m/2),floor(m/2)))))
  x <- if (m > 1) {
    stats::model.matrix.default(~ colData$condition)
  } else {
    cbind(rep(1,m),rep(0,m))
  }
  mu <- t(2^(x %*% t(beta)) * sizeFactors)
  countData <- matrix(rnbinom(m*n, mu=mu, size=1/dispersion), ncol=m)
  mode(countData) <- "integer"
  colnames(countData) <- paste("sample",1:m,sep="")
  rowRanges <- GRanges("1",IRanges(start=(1:n - 1) * 100 + 1,width=100))
  names(rowRanges) <- paste0("gene",1:n)

  # set environment to global environment,
  # to avoid the formula carrying with it all the objects
  # here including 'object' itself.
  design <- if (m > 1) {
    as.formula("~ condition", env=.GlobalEnv)
  } else {
    as.formula("~ 1", env=.GlobalEnv)
  }
  
  object <- DESeqDataSetFromMatrix(countData = countData,
                                   colData = colData,
                                   design = design,
                                   rowRanges = rowRanges)
  trueVals <- DataFrame(trueIntercept = beta[,1],
                        trueBeta = beta[,2],
                        trueDisp = dispersion)
  mcols(trueVals) <- DataFrame(type=rep("input",ncol(trueVals)),
                               description=c("simulated intercept values",
                                 "simulated beta values",
                                 "simulated dispersion values"))
  mcols(object) <- cbind(mcols(object),trueVals)
  return(object)
}


#' Low-level function to estimate size factors with robust regression.
#' 
#' Given a matrix or data frame of count data, this function estimates the size
#' factors as follows: Each column is divided by the geometric means of the
#' rows. The median (or, if requested, another location estimator) of these
#' ratios (skipping the genes with a geometric mean of zero) is used as the size
#' factor for this column. Typically, one will not call this function directly, but use
#' \code{\link{estimateSizeFactors}}.
#' 
#' @param counts a matrix or data frame of counts, i.e., non-negative integer
#' values
#' @param locfunc a function to compute a location for a sample. By default, the
#' median is used. However, especially for low counts, the
#' \code{\link[genefilter]{shorth}} function from genefilter may give better results.
#' @param geoMeans by default this is not provided, and the
#' geometric means of the counts are calculated within the function.
#' A vector of geometric means from another count matrix can be provided
#' for a "frozen" size factor calculation
#' @param controlGenes optional, numeric or logical index vector specifying those genes to
#' use for size factor estimation (e.g. housekeeping or spike-in genes)
#' @param type standard median ratio (\code{"ratio"}) or where the
#' geometric mean is only calculated over positive counts per row
#' (\code{"poscounts"})
#' @return a vector with the estimates size factors, one element per column
#' @author Simon Anders
#' @seealso \code{\link{estimateSizeFactors}}
#' @examples
#' 
#' dds <- makeExampleDESeqDataSet()
#' estimateSizeFactorsForMatrix(counts(dds))
#' geoMeans <- exp(rowMeans(log(counts(dds))))
#' estimateSizeFactorsForMatrix(counts(dds),geoMeans=geoMeans)
#' 
#' @export
estimateSizeFactorsForMatrix <- function(counts, locfunc=stats::median,
                                         geoMeans, controlGenes,
                                         type=c("ratio","poscounts")) {
  type <- match.arg(type, c("ratio","poscounts"))
  if (missing(geoMeans)) {
    incomingGeoMeans <- FALSE
    if (type == "ratio") {
      loggeomeans <- rowMeans(log(counts))
    } else if (type == "poscounts") {
      lc <- log(counts)
      lc[!is.finite(lc)] <- 0
      loggeomeans <- rowMeans(lc)
      allZero <- rowSums(counts) == 0
      loggeomeans[allZero] <- -Inf
    }
  } else {
    incomingGeoMeans <- TRUE
    if (length(geoMeans) != nrow(counts)) {
      stop("geoMeans should be as long as the number of rows of counts")
    }
    loggeomeans <- log(geoMeans)
  }
  if (all(is.infinite(loggeomeans))) {
    stop("every gene contains at least one zero, cannot compute log geometric means")
  }
  sf <- if (missing(controlGenes)) {
    apply(counts, 2, function(cnts) {
      exp(locfunc((log(cnts) - loggeomeans)[is.finite(loggeomeans) & cnts > 0]))
    })
  } else {
    if ( !( is.numeric(controlGenes) | is.logical(controlGenes) ) ) {
      stop("controlGenes should be either a numeric or logical vector")
    }
    loggeomeansSub <- loggeomeans[controlGenes]
    apply(counts[controlGenes,,drop=FALSE], 2, function(cnts) {
      exp(locfunc((log(cnts) - loggeomeansSub)[is.finite(loggeomeansSub) & cnts > 0]))
    })
  }
  if (incomingGeoMeans) {
    # stabilize size factors to have geometric mean of 1
    sf <- sf/exp(mean(log(sf)))
  }
  sf
}

#' Low-level functions to fit dispersion estimates
#'
#' Normal users should instead use \code{\link{estimateDispersions}}.
#' These low-level functions are called by \code{\link{estimateDispersions}},
#' but are exported and documented for non-standard usage.
#' For instance, it is possible to replace fitted values with a custom fit and continue
#' with the maximum a posteriori dispersion estimation, as demonstrated in the
#' examples below.
#'
#' @param object a DESeqDataSet
#' @param fitType either "parametric", "local", "mean", or "glmGamPoi"
#' for the type of fitting of dispersions to the mean intensity.
#' See \code{\link{estimateDispersions}} for description.
#' @param outlierSD the number of standard deviations of log
#' gene-wise estimates above the prior mean (fitted value),
#' above which dispersion estimates will be labelled
#' outliers. Outliers will keep their original value and
#' not be shrunk using the prior.
#' @param dispPriorVar the variance of the normal prior on the log dispersions.
#' If not supplied, this is calculated as the difference between
#' the mean squared residuals of gene-wise estimates to the
#' fitted dispersion and the expected sampling variance
#' of the log dispersion
#' @param minDisp small value for the minimum dispersion, to allow
#' for calculations in log scale, one order of magnitude above this value is used
#' as a test for inclusion in mean-dispersion fitting
#' @param kappa_0 control parameter used in setting the initial proposal
#' in backtracking search, higher kappa_0 results in larger steps
#' @param dispTol control parameter to test for convergence of log dispersion,
#' stop when increase in log posterior is less than dispTol
#' @param maxit control parameter: maximum number of iterations to allow for convergence
#' @param useCR whether to use Cox-Reid correction
#' @param weightThreshold threshold for subsetting the design matrix and GLM weights
#' for calculating the Cox-Reid correction
#' @param quiet whether to print messages at each step
#' @param modelMatrix for advanced use only,
#' a substitute model matrix for gene-wise and MAP dispersion estimation
#' @param niter number of times to iterate between estimation of means and
#' estimation of dispersion
#' @param linearMu estimate the expected counts matrix using a linear model,
#' default is NULL, in which case a lienar model is used if the
#' number of groups defined by the model matrix is equal to the number
#' of columns of the model matrix
#' @param minmu lower bound on the estimated count for fitting gene-wise dispersion
#' @param alphaInit initial guess for the dispersion estimate, alpha
#' @param type can either be "DESeq2" or "glmGamPoi". Specifies if the glmGamPoi
#' package is used to calculate the dispersion. This can be significantly faster
#' if there are many replicates with small counts.
#' 
#' @return a DESeqDataSet with gene-wise, fitted, or final MAP
#' dispersion estimates in the metadata columns of the object.
#' 
#' \code{estimateDispersionsPriorVar} is called inside of \code{estimateDispersionsMAP}
#' and stores the dispersion prior variance as an attribute of
#' \code{dispersionFunction(dds)}, which can be manually provided to
#' \code{estimateDispersionsMAP} for parallel execution.
#'
#' @aliases estimateDispersionsGeneEst estimateDispersionsFit estimateDispersionsMAP estimateDispersionsPriorVar
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' dds <- estimateSizeFactors(dds)
#' dds <- estimateDispersionsGeneEst(dds)
#' dds <- estimateDispersionsFit(dds)
#' dds <- estimateDispersionsMAP(dds)
#' plotDispEsts(dds) 
#'
#' # after having run estimateDispersionsFit()
#' # the dispersion prior variance over all genes
#' # can be obtained like so:
#' 
#' dispPriorVar <- estimateDispersionsPriorVar(dds)
#' 
#' @seealso \code{\link{estimateDispersions}}
#'
#' @export
estimateDispersionsGeneEst <- function(object, minDisp=1e-8, kappa_0=1,
                                       dispTol=1e-6, maxit=100, useCR=TRUE,
                                       weightThreshold=1e-2,
                                       quiet=FALSE,
                                       modelMatrix=NULL, niter=1, linearMu=NULL,
                                       minmu=if (type=="glmGamPoi") 1e-6 else 0.5,
                                       alphaInit=NULL,
                                       type = c("DESeq2", "glmGamPoi")) {
  
  type <- match.arg(type, c("DESeq2", "glmGamPoi"))
  if (!is.null(mcols(object)$dispGeneEst)) {
    if (!quiet) message("found already estimated gene-wise dispersions, removing these")
    removeCols <- c("dispGeneEst","dispGeneIter")
    mcols(object) <- mcols(object)[,!names(mcols(object)) %in% removeCols,drop=FALSE]
  }
  stopifnot(length(minDisp) == 1)
  stopifnot(length(kappa_0) == 1)
  stopifnot(length(dispTol) == 1)
  stopifnot(length(maxit) == 1)
  if (log(minDisp/10) <= -30) {
    stop("for computational stability, log(minDisp/10) should be above -30")
  }

  # in case the class of the mcols(mcols(object)) are not character
  object <- sanitizeRowRanges(object)

  if (is.null(modelMatrix)) {
    modelMatrix <- getModelMatrix(object) 
  }
  checkFullRank(modelMatrix)
  if (nrow(modelMatrix) == ncol(modelMatrix)) {
    stop("the number of samples and the number of model coefficients are equal,
  i.e., there are no replicates to estimate the dispersion.
  use an alternate design formula")
  }
  
  object <- getBaseMeansAndVariances(object)

  # use weights if they are present in assays(object)
  # (we need this already to decide about linear mu fitting)
  attr(object, "weightsOK") <- NULL # reset any information
  wlist <- getAndCheckWeights(object, modelMatrix, weightThreshold=weightThreshold)
  object <- wlist$object
  weights <- wlist$weights
  # don't let weights go below 1e-6
  weights <- pmax(weights, 1e-6)
  useWeights <- wlist$useWeights
  
  # only continue on the rows with non-zero row mean
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]
  weights <- weights[!mcols(object)$allZero,,drop=FALSE]

  if (is.null(alphaInit)) {
    # this rough dispersion estimate (alpha_hat)
    # is for estimating mu
    # and for the initial starting point for line search
    roughDisp <- roughDispEstimate(y = counts(objectNZ,normalized=TRUE),
                                   x = modelMatrix)
    momentsDisp <- momentsDispEstimate(objectNZ)
    alpha_hat <- pmin(roughDisp, momentsDisp)
  } else {
    if (length(alphaInit) == 1) {
      alpha_hat <- rep(alphaInit, nrow(objectNZ))
    } else {
      stopifnot(length(alphaInit) == nrow(objectNZ))
      alpha_hat <- alphaInit
    }
  }

  # bound the rough estimated alpha between minDisp and maxDisp for numeric stability
  maxDisp <- max(10, ncol(object))
  alpha_hat <- alpha_hat_new <- alpha_init <- pmin(pmax(minDisp, alpha_hat), maxDisp)

  stopifnot(length(niter) == 1 & niter > 0)
  
  # use a linear model to estimate the expected counts
  # if the number of groups according to the model matrix
  # is equal to the number of columns
  if (is.null(linearMu)) {
    modelMatrixGroups <- modelMatrixGroups(modelMatrix)
    linearMu <- nlevels(modelMatrixGroups) == ncol(modelMatrix)
    # also check for weights (then can't do linear mu)
    if (useWeights) {
      linearMu <- FALSE
    }
  }
  
  # below, iterate between mean and dispersion estimation (niter) times
  fitidx <- rep(TRUE,nrow(objectNZ))
  mu <- matrix(0, nrow=nrow(objectNZ), ncol=ncol(objectNZ))
  dispIter <- numeric(nrow(objectNZ))
  # bound the estimated count by 'minmu'
  # this helps make the fitting more robust,
  # because 1/mu occurs in the weights for the NB GLM
  for (iter in seq_len(niter)) {
    if (!linearMu) {
      fit <- fitNbinomGLMs(objectNZ[fitidx,,drop=FALSE],
                           alpha_hat=alpha_hat[fitidx],
                           modelMatrix=modelMatrix, type=type)
      fitMu <- fit$mu
    } else {
      fitMu <- linearModelMuNormalized(objectNZ[fitidx,,drop=FALSE],
                                       modelMatrix)
    }
    fitMu[fitMu < minmu] <- minmu
    mu[fitidx,] <- fitMu
    
    
    # use of kappa_0 in backtracking search
    # initial proposal = log(alpha) + kappa_0 * deriv. of log lik. w.r.t. log(alpha)
    # use log(minDisp/10) to stop if dispersions going to -infinity
    if (type == "DESeq2") {
      dispRes <- fitDispWrapper(ySEXP = counts(objectNZ)[fitidx,,drop=FALSE],
                                xSEXP = modelMatrix,
                                mu_hatSEXP = fitMu,
                                log_alphaSEXP = log(alpha_hat)[fitidx],
                                log_alpha_prior_meanSEXP = log(alpha_hat)[fitidx],
                                log_alpha_prior_sigmasqSEXP = 1, min_log_alphaSEXP = log(minDisp/10),
                                kappa_0SEXP = kappa_0, tolSEXP = dispTol,
                                maxitSEXP = maxit, usePriorSEXP = FALSE,
                                weightsSEXP = weights,
                                useWeightsSEXP = useWeights,
                                weightThresholdSEXP = weightThreshold,
                                useCRSEXP = useCR)
      
      dispIter[fitidx] <- dispRes$iter
      alpha_hat_new[fitidx] <- pmin(exp(dispRes$log_alpha), maxDisp)
      last_lp <- dispRes$last_lp
      initial_lp <- dispRes$initial_lp
      # only rerun those rows which moved
    } else if (type == "glmGamPoi") {
      if (!requireNamespace("glmGamPoi", quietly=TRUE)) {
        stop("type='glmGamPoi' requires installing the Bioconductor package 'glmGamPoi'")
      }
      if (!quiet) message("using 'glmGamPoi' as fitType. If used in published research, please cite:
    Ahlmann-Eltze, C., Huber, W. (2020) glmGamPoi: Fitting Gamma-Poisson
    Generalized Linear Models on Single Cell Count Data. Bioinformatics.
    https://doi.org/10.1093/bioinformatics/btaa1009")
      Counts <- counts(objectNZ)
      initial_lp <- vapply(which(fitidx), function(idx){
        sum(dnbinom(Counts[idx, ], mu = fitMu[idx, ], size = 1 / alpha_hat[idx], log = TRUE))
      }, FUN.VALUE = 0.0)
      dispersion_fits <- glmGamPoi::overdispersion_mle(Counts[fitidx, ], mean = fitMu[fitidx, ],
                                                       model_matrix = modelMatrix, verbose = ! quiet)
      dispIter[fitidx] <- dispersion_fits$iterations
      alpha_hat_new[fitidx] <- pmin(dispersion_fits$estimate, maxDisp)
      last_lp <- vapply(which(fitidx), function(idx){
        sum(dnbinom(Counts[idx, ], mu = fitMu[idx, ], size = 1 / alpha_hat_new[idx], log = TRUE))
      }, FUN.VALUE = 0.0)
    }
    fitidx <- abs(log(alpha_hat_new) - log(alpha_hat)) > .05
    alpha_hat <- alpha_hat_new
    if (sum(fitidx) == 0) break
  }
  # dont accept moves if the log posterior did not
  # increase by more than one millionth,
  # and set the small estimates to the minimum dispersion
  dispGeneEst <- alpha_hat
  if (niter == 1) {
    noIncrease <- last_lp < initial_lp + abs(initial_lp)/1e6
    dispGeneEst[which(noIncrease)] <- alpha_init[which(noIncrease)]
  }
  # didn't reach the maxmium and iterated more than once
  dispGeneEstConv <- dispIter < maxit & !(dispIter == 1)

  # if lacking convergence from fitDisp() (C++)...
  refitDisp <- !dispGeneEstConv & dispGeneEst > minDisp*10
  if (sum(refitDisp) > 0) {
    dispGrid <- fitDispGridWrapper(y = counts(objectNZ)[refitDisp,,drop=FALSE],
                                   x = modelMatrix,
                                   mu = mu[refitDisp,,drop=FALSE],
                                   logAlphaPriorMean = rep(0,sum(refitDisp)),
                                   logAlphaPriorSigmaSq = 1, usePrior = FALSE,
                                   weightsSEXP = weights[refitDisp,,drop=FALSE],
                                   useWeightsSEXP = useWeights,
                                   weightThresholdSEXP = weightThreshold,
                                   useCRSEXP = useCR)
    dispGeneEst[refitDisp] <- dispGrid
  }
  dispGeneEst <- pmin(pmax(dispGeneEst, minDisp), maxDisp)
  
  dispDataFrame <- buildDataFrameWithNARows(list(dispGeneEst=dispGeneEst,
                                                 dispGeneIter=dispIter),
                                            mcols(object)$allZero)
  mcols(dispDataFrame) <- DataFrame(type=rep("intermediate",ncol(dispDataFrame)),
                                    description=c("gene-wise estimates of dispersion",
                                                  "number of iterations for gene-wise"))
  mcols(object) <- cbind(mcols(object), dispDataFrame)
  assays(object, withDimnames=FALSE)[["mu"]] <- buildMatrixWithNARows(mu, mcols(object)$allZero)
  
  return(object)
}

#' @rdname estimateDispersionsGeneEst
#' @export
estimateDispersionsFit <- function(object,fitType=c("parametric","local","mean", "glmGamPoi"),
                                   minDisp=1e-8, quiet=FALSE) {

  if (is.null(mcols(object)$allZero)) {
    object <- getBaseMeansAndVariances(object)
  }
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]
  useForFit <- mcols(objectNZ)$dispGeneEst > 100*minDisp
  if (sum(useForFit) == 0) {
    stop("all gene-wise dispersion estimates are within 2 orders of magnitude
  from the minimum value, and so the standard curve fitting techniques will not work.
  One can instead use the gene-wise estimates as final estimates:
  dds <- estimateDispersionsGeneEst(dds)
  dispersions(dds) <- mcols(dds)$dispGeneEst
  ...then continue with testing using nbinomWaldTest or nbinomLRT")
  }
  
  fitType <- match.arg(fitType, choices=c("parametric","local","mean", "glmGamPoi"))
  stopifnot(length(fitType)==1)
  stopifnot(length(minDisp)==1)
  if (fitType == "parametric") {
    trial <- try(dispFunction <- parametricDispersionFit(mcols(objectNZ)$baseMean[useForFit],
                                                         mcols(objectNZ)$dispGeneEst[useForFit]),
                 silent=TRUE)
    if (inherits(trial,"try-error")) {
      message("-- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.")
      fitType <- "local"
    }
  }
  if (fitType == "local") {
    dispFunction <- localDispersionFit(means = mcols(objectNZ)$baseMean[useForFit],
                                       disps = mcols(objectNZ)$dispGeneEst[useForFit],
                                       minDisp = minDisp)
  }
  if (fitType == "mean") {
    useForMean <- mcols(objectNZ)$dispGeneEst > 10*minDisp
    meanDisp <- mean(mcols(objectNZ)$dispGeneEst[useForMean],na.rm=TRUE,trim=0.001)
    dispFunction <- function(means) meanDisp
    attr( dispFunction, "mean" ) <- meanDisp
  }
  if (fitType == "glmGamPoi") {
    if (!requireNamespace("glmGamPoi", quietly=TRUE)) {
      stop("type='glmGamPoi' requires installing the Bioconductor package 'glmGamPoi'")
    }
    base_means <- mcols(objectNZ)$baseMean[useForFit]
    median_fit <- glmGamPoi::loc_median_fit(base_means, 
                                            mcols(objectNZ)$dispGeneEst[useForFit])
    get_closest_index <- function(x, vec){
      iv <- findInterval(x, vec)
      dist_left <- x - vec[ifelse(iv == 0, NA, iv)]
      dist_right <- vec[iv + 1] - x
      ifelse(! is.na(dist_left) & (is.na(dist_right) | dist_left < dist_right), iv, iv + 1)
    }
    sorted_bm <- sort(base_means)
    ordered_medians <- median_fit[order(base_means)]
    dispFunction <- function(means){
      indices <- get_closest_index(means, sorted_bm)
      ordered_medians[indices]
    }
  }
  if (!(fitType %in% c("parametric","local","mean", "glmGamPoi"))) {
    stop("unknown fitType")
  }
 
  # store the dispersion function and attributes
  attr( dispFunction, "fitType" ) <- fitType
  if (quiet) {
    suppressMessages({ dispersionFunction(object) <- dispFunction })
  } else {
    dispersionFunction(object) <- dispFunction
  }
  
  return(object)
}

#' @rdname estimateDispersionsGeneEst
#' @export
estimateDispersionsMAP <- function(object, outlierSD=2, dispPriorVar,
                                   minDisp=1e-8, kappa_0=1, dispTol=1e-6,
                                   maxit=100, useCR=TRUE,
                                   weightThreshold=1e-2,
                                   modelMatrix=NULL, 
                                   type = c("DESeq2", "glmGamPoi"),
                                   quiet=FALSE) {
  stopifnot(length(outlierSD)==1)
  stopifnot(length(minDisp)==1)
  stopifnot(length(kappa_0)==1)
  stopifnot(length(dispTol)==1)
  stopifnot(length(maxit)==1)
  type <- match.arg(type, c("DESeq2", "glmGamPoi"))
  if (is.null(mcols(object)$allZero)) {
    object <- getBaseMeansAndVariances(object)
  }
  if (!is.null(mcols(object)$dispersion)) {
    if (!quiet) message("found already estimated dispersions, removing these")
    removeCols <- c("dispersion","dispOutlier","dispMAP","dispIter","dispConv")
    mcols(object) <- mcols(object)[,!names(mcols(object)) %in% removeCols,drop=FALSE]
  }

  if (is.null(modelMatrix)) {
    modelMatrix <- getModelMatrix(object)
  }
  
  # fill in the calculated dispersion prior variance
  if (missing(dispPriorVar)) {
    # if no gene-wise estimates above minimum
    if (sum(mcols(object)$dispGeneEst >= minDisp*100,na.rm=TRUE) == 0) {
      warning(paste0("all genes have dispersion estimates < ",minDisp*10,
                     ", returning disp = ",minDisp*10))
      resultsList <- list(dispersion = rep(minDisp*10, sum(!mcols(object)$allZero)))
      dispDataFrame <- buildDataFrameWithNARows(resultsList, mcols(object)$allZero)
      mcols(dispDataFrame) <- DataFrame(type="intermediate",
                                        description="final estimates of dispersion")
      mcols(object) <- cbind(mcols(object), dispDataFrame)
      dispFn <- dispersionFunction(object)
      attr( dispFn, "dispPriorVar" ) <- 0.25
      dispersionFunction(object, estimateVar=FALSE) <- dispFn
      return(object)
    }
    dispPriorVar <- estimateDispersionsPriorVar(object, modelMatrix=modelMatrix)
    dispFn <- dispersionFunction(object)
    attr( dispFn, "dispPriorVar" ) <- dispPriorVar
    dispersionFunction(object, estimateVar=FALSE) <- dispFn
  } else {
    dispFn <- dispersionFunction(object)
    attr( dispFn, "dispPriorVar" ) <- dispPriorVar
    dispersionFunction(object, estimateVar=FALSE) <- dispFn
  }

  stopifnot(length(dispPriorVar)==1)

  # use weights if they are present in assays(object)
  wlist <- getAndCheckWeights(object, modelMatrix, weightThreshold=weightThreshold)
  object <- wlist$object
  weights <- wlist$weights
  useWeights <- wlist$useWeights
  
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]
  weights <- weights[!mcols(object)$allZero,,drop=FALSE]
  varLogDispEsts <- attr( dispersionFunction(object), "varLogDispEsts" )
  
  # set prior variance for fitting dispersion
  log_alpha_prior_sigmasq <- dispPriorVar

  # get previously calculated mu
  mu <- assays(objectNZ)[["mu"]]
  
  if (type == "DESeq2" ) {
    # start fitting at gene estimate unless the points are one order of magnitude
    # below the fitted line, then start at fitted line
    dispInit <- ifelse(mcols(objectNZ)$dispGeneEst >  0.1 * mcols(objectNZ)$dispFit,
                       mcols(objectNZ)$dispGeneEst,
                       mcols(objectNZ)$dispFit)
  
    # if any missing values, fill in the fitted value to initialize
    dispInit[is.na(dispInit)] <- mcols(objectNZ)$dispFit[is.na(dispInit)]
    
    # run with prior
    dispResMAP <- fitDispWrapper(ySEXP = counts(objectNZ),
                                 xSEXP = modelMatrix,
                                 mu_hatSEXP = mu,
                                 log_alphaSEXP = log(dispInit),
                                 log_alpha_prior_meanSEXP = log(mcols(objectNZ)$dispFit),
                                 log_alpha_prior_sigmasqSEXP = log_alpha_prior_sigmasq,
                                 min_log_alphaSEXP = log(minDisp/10),
                                 kappa_0SEXP = kappa_0, tolSEXP = dispTol,
                                 maxitSEXP = maxit, usePriorSEXP = TRUE,
                                 weightsSEXP = weights,
                                 useWeightsSEXP = useWeights,
                                 weightThresholdSEXP = weightThreshold,
                                 useCRSEXP = useCR)
  
    # prepare dispersions for storage in mcols(object)
    dispMAP <- exp(dispResMAP$log_alpha) 
    dispIter <- dispResMAP$iter
    
    # when lacking convergence from fitDisp() (C++)
    # we use a function to maximize dispersion parameter
    # along an adaptive grid (also C++)
    dispConv <- dispResMAP$iter < maxit
    refitDisp <- !dispConv
    if (sum(refitDisp) > 0) {
      dispGrid <- fitDispGridWrapper(y = counts(objectNZ)[refitDisp,,drop=FALSE],
                                     x = modelMatrix,
                                     mu = mu[refitDisp,,drop=FALSE],
                                     logAlphaPriorMean = log(mcols(objectNZ)$dispFit)[refitDisp],
                                     logAlphaPriorSigmaSq = log_alpha_prior_sigmasq,
                                     usePrior=TRUE,
                                     weightsSEXP = weights[refitDisp,,drop=FALSE],
                                     useWeightsSEXP = useWeights,
                                     weightThresholdSEXP = weightThreshold,
                                     useCRSEXP=TRUE)
      dispMAP[refitDisp] <- dispGrid
      
    }
  } else if (type == "glmGamPoi") {
    if (!requireNamespace("glmGamPoi", quietly=TRUE)) {
      stop("type='glmGamPoi' requires installing the Bioconductor package 'glmGamPoi'")
    }
    stopifnot("type = 'glmGamPoi' cannot handle weights" = ! useWeights)
    gene_means <- mcols(objectNZ)$baseMean
    disp_est <- mcols(objectNZ)$dispGeneEst
    disp_trend <- mcols(objectNZ)$dispFit
    shrink_res <- glmGamPoi::overdispersion_shrinkage(disp_est, gene_means = gene_means, 
                                        df = ncol(objectNZ) - ncol(modelMatrix),
                                        disp_trend = disp_trend)
    dispFitCorrected <- (shrink_res$ql_disp_trend * (gene_means + gene_means^2 * disp_trend) - gene_means) / gene_means^2
    dispFitCorrected <- pmin(pmax(dispFitCorrected, minDisp), max(10, ncol(object)))
    
    qlResultsList <- list(qlDispMLE = shrink_res$ql_disp_estimate,
                          qlDispFit = shrink_res$ql_disp_trend,
                          qlDispMAP = shrink_res$ql_disp_shrunken,
                          dispFitQLCorrected = dispFitCorrected)
    
    qlDispDataFrame <- buildDataFrameWithNARows(qlResultsList, mcols(object)$allZero)
    mcols(qlDispDataFrame) <- DataFrame(type=rep("intermediate",ncol(qlDispDataFrame)),
                                      description=c("quasi likelihood dispersion MLE",
                                                    "quasi likelihood dispersion Trend",
                                                    "quasi likelihood dispersion MAP",
                                                    "dispersion trend corrected by quasi likelihood"))
    
    mcols(object) <- cbind(mcols(object), qlDispDataFrame)
    attr( object, "quasiLikelihood_df0" ) <- shrink_res$ql_df0
    # Quick way to find alpha that would give same variance as shrunken quasi
    # likelihood dispersion with dispFit
    dispMAP <- (shrink_res$ql_disp_shrunken * (gene_means + gene_means^2 * disp_trend) - gene_means) / gene_means^2
    dispIter <- rep(0, length(dispMAP))
  }
  
  
  # bound the dispersion estimate between minDisp and maxDisp for numeric stability
  maxDisp <- max(10, ncol(object))
  dispMAP <- pmin(pmax(dispMAP, minDisp), maxDisp)
  
  dispersionFinal <- dispMAP
  
  # detect outliers which have gene-wise estimates
  # outlierSD * standard deviation of log gene-wise estimates
  # above the fitted mean (prior mean)
  # and keep the original gene-est value for these.
  # Note: we use the variance of log dispersions estimates
  # from all the genes, not only those from below
  dispOutlier <- log(mcols(objectNZ)$dispGeneEst) >
                 log(mcols(objectNZ)$dispFit) +
                 outlierSD * sqrt(varLogDispEsts)
  dispOutlier[is.na(dispOutlier)] <- FALSE
  dispersionFinal[dispOutlier] <- mcols(objectNZ)$dispGeneEst[dispOutlier]
 
  resultsList <- list(dispersion = dispersionFinal,
                      dispIter = dispIter,
                      dispOutlier = dispOutlier,
                      dispMAP = dispMAP)

  dispDataFrame <- buildDataFrameWithNARows(resultsList, mcols(object)$allZero)
  mcols(dispDataFrame) <- DataFrame(type=rep("intermediate",ncol(dispDataFrame)),
                                    description=c("final estimate of dispersion",
                                      "number of iterations",
                                      "dispersion flagged as outlier",
                                      "maximum a posteriori estimate"))

  mcols(object) <- cbind(mcols(object), dispDataFrame)
  return(object)
}

#' @rdname estimateDispersionsGeneEst
#' @export
estimateDispersionsPriorVar <- function(object, minDisp=1e-8, modelMatrix=NULL) {
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]
  aboveMinDisp <- mcols(objectNZ)$dispGeneEst >= minDisp*100
  if (is.null(modelMatrix)) {
    modelMatrix <- getModelMatrix(object)
  }
  # estimate the variance of the distribution of the
  # log dispersion estimates around the fitted value
  dispResiduals <- log(mcols(objectNZ)$dispGeneEst) - log(mcols(objectNZ)$dispFit)
  if (sum(aboveMinDisp,na.rm=TRUE) == 0) {
    stop("no data found which is greater than minDisp")
  }
  
  varLogDispEsts <- attr(dispersionFunction(object), "varLogDispEsts")
  
  m <- nrow(modelMatrix)
  p <- ncol(modelMatrix)

  # if the residual degrees of freedom is between 1 and 3, the distribution
  # of log dispersions is especially asymmetric and poorly estimated
  # by the MAD. we then use an alternate estimator, a monte carlo
  # approach to match the distribution
  if (((m - p) <= 3) & (m > p)) {
    # in order to produce identical results we set the seed, 
    # and so we need to save and restore the .Random.seed value first
    if (exists(".Random.seed")) {
      oldRandomSeed <- .Random.seed
    }
    set.seed(2)
    # The residuals are the observed distribution we try to match
    obsDist <- dispResiduals[aboveMinDisp]
    brks <- -20:20/2
    obsDist <- obsDist[obsDist > min(brks) & obsDist < max(brks)]
    obsVarGrid <- seq(from=0,to=8,length=200)
    obsDistHist <- hist(obsDist,breaks=brks,plot=FALSE)
    klDivs <- sapply(obsVarGrid, function(x) {
      randDist <- log(rchisq(1e4,df=(m-p))) + rnorm(1e4,0,sqrt(x)) - log(m - p)
      randDist <- randDist[randDist > min(brks) & randDist < max(brks)]
      randDistHist <- hist(randDist,breaks=brks,plot=FALSE)
      z <- c(obsDistHist$density,randDistHist$density)
      small <- min(z[z > 0])
      kl <- sum(obsDistHist$density * (log(obsDistHist$density + small) - log(randDistHist$density + small)))
      kl
    })
    lofit <- loess(klDivs ~ obsVarGrid, span=.2)
    obsVarFineGrid <- seq(from=0,to=8,length=1000)
    lofitFitted <- predict(lofit,obsVarFineGrid)
    argminKL <- obsVarFineGrid[which.min(lofitFitted)]
    expVarLogDisp <- trigamma((m - p)/2)
    dispPriorVar <- pmax(argminKL, 0.25)
    # finally, restore the .Random.seed if it existed beforehand
    if (exists("oldRandomSeed")) {
      .Random.seed <<- oldRandomSeed
    }

    return(dispPriorVar)
  }

  # estimate the expected sampling variance of the log estimates
  # Var(log(cX)) = Var(log(X))
  # X ~ chi-squared with m - p degrees of freedom
  if (m > p) {
    expVarLogDisp <- trigamma((m - p)/2)
    # set the variance of the prior using these two estimates
    # with a minimum of .25
    dispPriorVar <- pmax((varLogDispEsts - expVarLogDisp), 0.25)
  } else {
    # we have m = p, so do not try to subtract sampling variance
    dispPriorVar <- varLogDispEsts
    expVarLogDisp <- 0
  }

  dispPriorVar
}



#' Wald test for the GLM coefficients
#' 
#' This function tests for significance of coefficients in a Negative
#' Binomial GLM, using previously calculated \code{\link{sizeFactors}}
#' (or \code{\link{normalizationFactors}})
#' and dispersion estimates.  See \code{\link{DESeq}} for the GLM formula.
#' 
#' The fitting proceeds as follows: standard maximum likelihood estimates
#' for GLM coefficients (synonymous with "beta", "log2 fold change", "effect size")
#' are calculated.
#' Then, optionally, a zero-centered Normal prior distribution 
#' (\code{betaPrior}) is assumed for the coefficients other than the intercept.
#'
#' Note that this posterior log2 fold change
#' estimation is now not the default setting for \code{nbinomWaldTest},
#' as the standard workflow for coefficient shrinkage has moved to
#' an additional function \code{link{lfcShrink}}.
#'
#' For calculating Wald test p-values, the coefficients are scaled by their
#' standard errors and then compared to a standard Normal distribution. 
#' The \code{\link{results}}
#' function without any arguments will automatically perform a contrast of the
#' last level of the last variable in the design formula over the first level.
#' The \code{contrast} argument of the \code{\link{results}} function can be used
#' to generate other comparisons.
#'  
#' The Wald test can be replaced with the \code{\link{nbinomLRT}}
#' for an alternative test of significance.
#' 
#' Notes on the log2 fold change prior:
#' 
#' The variance of the prior distribution for each
#' non-intercept coefficient is calculated using the observed
#' distribution of the maximum likelihood coefficients.  
#' The final coefficients are then maximum a posteriori estimates
#' using this prior (Tikhonov/ridge regularization). 
#' See below for details on the
#' prior variance and the Methods section of the DESeq2 manuscript for more detail.
#' The use of a prior has little effect on genes with high counts and helps to
#' moderate the large spread in coefficients for genes with low counts.
#'
#' The prior variance is calculated by matching the 0.05 upper quantile
#' of the observed MLE coefficients to a zero-centered Normal distribution.
#' In a change of methods since the 2014 paper,
#' the weighted upper quantile is calculated using the
#' \code{wtd.quantile} function from the Hmisc package
#' (function has been copied into DESeq2 to avoid extra dependencies).
#' The weights are the inverse of the expected variance of log counts, so the inverse of
#' \eqn{1/\bar{\mu} + \alpha_{tr}}{1/mu-bar + alpha_tr} using the mean of
#' normalized counts and the trended dispersion fit. The weighting ensures
#' that noisy estimates of log fold changes from small count genes do not
#' overly influence the calculation of the prior variance.
#' See \code{\link{estimateBetaPriorVar}}.
#' The final prior variance for a factor level is the average of the
#' estimated prior variance over all contrasts of all levels of the factor. 
#'
#' When a log2 fold change prior is used (betaPrior=TRUE),
#' then \code{nbinomWaldTest} will by default use expanded model matrices,
#' as described in the \code{modelMatrixType} argument, unless this argument
#' is used to override the default behavior.
#' This ensures that log2 fold changes will be independent of the choice
#' of reference level. In this case, the beta prior variance for each factor
#' is calculated as the average of the mean squared maximum likelihood
#' estimates for each level and every possible contrast. 
#'
#' @param object a DESeqDataSet
#' @param betaPrior whether or not to put a zero-mean normal prior on
#' the non-intercept coefficients
#' @param betaPriorVar a vector with length equal to the number of
#' model terms including the intercept.
#' betaPriorVar gives the variance of the prior on the sample betas
#' on the log2 scale. if missing (default) this is estimated from the data
#' @param modelMatrix an optional matrix, typically this is set to NULL
#' and created within the function
#' @param modelMatrixType either "standard" or "expanded", which describe
#' how the model matrix, X of the formula in \code{\link{DESeq}}, is
#' formed. "standard" is as created by \code{model.matrix} using the
#' design formula. "expanded" includes an indicator variable for each
#' level of factors in addition to an intercept.
#' betaPrior must be set to TRUE in order for expanded model matrices
#' to be fit.
#' @param betaTol control parameter defining convergence
#' @param maxit the maximum number of iterations to allow for convergence of the
#' coefficient vector
#' @param useOptim whether to use the native optim function on rows which do not
#' converge within maxit
#' @param quiet whether to print messages at each step
#' @param useT whether to use a t-distribution as a null distribution,
#' for significance testing of the Wald statistics.
#' If FALSE, a standard normal null distribution is used.
#' See next argument \code{df} for information about which t is used.
#' If \code{useT=TRUE} then further calls to \code{\link{results}}
#' will make use of \code{mcols(object)$tDegreesFreedom} that is stored
#' by \code{nbinomWaldTest}.
#' @param df the degrees of freedom for the t-distribution.
#' This can be of length 1 or the number of rows of \code{object}.
#' If this is not specified, the degrees of freedom will be set
#' by the number of samples minus the number of columns of the design
#' matrix used for dispersion estimation. If \code{"weights"} are included in
#' the \code{assays(object)}, then the sum of the weights is used in lieu
#' of the number of samples.
#' @param useQR whether to use the QR decomposition on the design
#' matrix X while fitting the GLM
#' @param minmu lower bound on the estimated count while fitting the GLM
#'
#' @return a DESeqDataSet with results columns accessible
#' with the \code{\link{results}} function.  The coefficients and standard errors are
#' reported on a log2 scale.
#'
#' @seealso \code{\link{DESeq}}, \code{\link{nbinomLRT}}
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' dds <- estimateSizeFactors(dds)
#' dds <- estimateDispersions(dds)
#' dds <- nbinomWaldTest(dds)
#' res <- results(dds)
#'
#' @export
nbinomWaldTest <- function(object,
                           betaPrior=FALSE, betaPriorVar,
                           modelMatrix=NULL, modelMatrixType,
                           betaTol=1e-8, maxit=100, useOptim=TRUE, quiet=FALSE,
                           useT=FALSE, df, useQR=TRUE, minmu=0.5) {
  if (is.null(dispersions(object))) {
    stop("testing requires dispersion estimates, first call estimateDispersions()")
  }
  stopifnot(length(maxit)==1)
  # in case the class of the mcols(mcols(object)) are not character
  object <- sanitizeRowRanges(object)
  
  if ("results" %in% mcols(mcols(object))$type) {
    if (!quiet) message("found results columns, replacing these")
    object <- removeResults(object)
  }
  if (is.null(mcols(object)$allZero)) {
    object <- getBaseMeansAndVariances(object)
  }
  
  # only continue on the rows with non-zero row mean
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]

  # model matrix not provided...
  if (is.null(modelMatrix)) {
    modelAsFormula <- TRUE
    termsOrder <- attr(terms.formula(design(object)),"order")
    interactionPresent <- any(termsOrder > 1)
    if (missing(betaPrior)) {
      betaPrior <- FALSE
    }

    # run some tests common to DESeq, nbinomWaldTest, nbinomLRT
    designAndArgChecker(object, betaPrior)

    # what kind of model matrix to use
    stopifnot(is.logical(betaPrior))
    blindDesign <- design(object) == formula(~ 1)
    if (blindDesign) {
      betaPrior <- FALSE
    }
    if (missing(modelMatrixType) || is.null(modelMatrixType)) {
      modelMatrixType <- if (betaPrior) {
        "expanded"
      } else {
        "standard"
      }
    }
    if (modelMatrixType == "expanded" & !betaPrior) {
      stop("expanded model matrices require a beta prior")
    }
    # store modelMatrixType so it can be accessed by estimateBetaPriorVar
    attr(object, "modelMatrixType") <- modelMatrixType
    hasIntercept <- attr(terms(design(object)),"intercept") == 1
    renameCols <- hasIntercept
  } else {
    # modelMatrix is not NULL, user-supplied
    if (missing(betaPrior)) {
      betaPrior <- FALSE
    }
    if (betaPrior) {
      if (missing(betaPriorVar)) stop("user-supplied model matrix with betaPrior=TRUE requires supplying betaPriorVar")
    }
    modelAsFormula <- FALSE
    attr(object, "modelMatrixType") <- "user-supplied"
    renameCols <- FALSE
  }

  if (!betaPrior) {
    # fit the negative binomial GLM without a prior
    # (in actuality a very wide prior with standard deviation 1e3 on log2 fold changes)
    fit <- fitNbinomGLMs(objectNZ,
                         betaTol=betaTol, maxit=maxit,
                         useOptim=useOptim, useQR=useQR,
                         renameCols=renameCols,
                         modelMatrix=modelMatrix,
                         minmu=minmu)
    H <- fit$hat_diagonals
    mu <- fit$mu
    modelMatrix <- fit$modelMatrix
    modelMatrixNames <- fit$modelMatrixNames
    # record the wide prior variance which was used in fitting
    betaPriorVar <- rep(1e6, ncol(fit$modelMatrix))
  } else {
    priorFitList <- fitGLMsWithPrior(object=object,
                                     betaTol=betaTol, maxit=maxit,
                                     useOptim=useOptim, useQR=useQR,
                                     betaPriorVar=betaPriorVar,
                                     modelMatrix=modelMatrix,
                                     minmu=minmu)
    fit <- priorFitList$fit
    H <- priorFitList$H
    mu <- priorFitList$mu
    betaPriorVar <- priorFitList$betaPriorVar
    modelMatrix <- priorFitList$modelMatrix
    mleBetaMatrix <- priorFitList$mleBetaMatrix

    # will add the MLE betas, so remove any which exist already
    # (possibly coming from estimateMLEForBetaPriorVar)
    mcols(object) <- mcols(object)[,grep("MLE_",names(mcols(object)),invert=TRUE)]
  }

  # store 'mu' and 'H', the hat matrix diagonals
  dimnames(mu) <- NULL
  assays(objectNZ, withDimnames=FALSE)[["mu"]] <- mu
  assays(object, withDimnames=FALSE)[["mu"]] <- buildMatrixWithNARows(mu, mcols(object)$allZero)
  dimnames(H) <- NULL
  assays(objectNZ, withDimnames=FALSE)[["H"]] <- H
  assays(object, withDimnames=FALSE)[["H"]] <- buildMatrixWithNARows(H, mcols(object)$allZero)
  
  # store the prior variance directly as an attribute
  # of the DESeqDataSet object, so it can be pulled later by
  # the results function (necessary for setting max Cook's distance)
  attr(object,"betaPrior") <- betaPrior
  attr(object,"betaPriorVar") <- betaPriorVar
  attr(object,"modelMatrix") <- modelMatrix
  attr(object,"test") <- "Wald"

  # calculate Cook's distance
  dispModelMatrix <- if (modelAsFormula) {
    getModelMatrix(object)
  } else {
    modelMatrix
  }
  attr(object,"dispModelMatrix") <- dispModelMatrix
  cooks <- calculateCooksDistance(objectNZ, H, dispModelMatrix)

  # record maximum Cook's
  maxCooks <- recordMaxCooks(design(object), colData(object), dispModelMatrix, cooks, nrow(objectNZ))

  # store Cook's distance for each sample
  assays(object, withDimnames=FALSE)[["cooks"]] <- buildMatrixWithNARows(cooks, mcols(object)$allZero)
  
  # add betas, standard errors and Wald p-values to the object
  modelMatrixNames <- colnames(modelMatrix)
  betaMatrix <- fit$betaMatrix
  colnames(betaMatrix) <- modelMatrixNames
  betaSE <- fit$betaSE
  colnames(betaSE) <- paste0("SE_",modelMatrixNames)
  WaldStatistic <- betaMatrix/betaSE
  colnames(WaldStatistic) <- paste0("WaldStatistic_",modelMatrixNames)

  #################################
  ## t distribution for p-values ##
  #################################
  
  if (useT) {
    # if the `df` was provided to nbinomWaldTest...
    if (!missing(df)) {
      stopifnot(length(df) == 1 | length(df) == nrow(object))
      if (length(df) == 1) {
        df <- rep(df, nrow(objectNZ))
      } else {
        # the `WaldStatistic` vector is along nonzero rows of `object`
        df <- df[!mcols(object)$allZero]
      }
    } else {
      # df was missing, so compute it from the number of samples (w.r.t. weights)
      # and the number of coefficients
      if ("weights" %in% assayNames(object)) {
        # this checks that weights are OK and normalizes to have rowMax == 1
        # (although this has already happened earlier in estDispGeneEst and estDispMAP...
        wlist <- getAndCheckWeights(objectNZ, dispModelMatrix)
        num.samps <- rowSums(wlist$weights)
      } else {
        num.samps <- rep(ncol(object), nrow(objectNZ))
      }
      df <- num.samps - ncol(dispModelMatrix)
    }
    df <- ifelse(df > 0, df, NA)
    stopifnot(length(df) == nrow(WaldStatistic))
    # use a t distribution to calculate the p-value
    WaldPvalue <- 2*pt(abs(WaldStatistic),df=df,lower.tail=FALSE)
  } else {
    # the default DESeq2 p-value: use the standard Normal
    WaldPvalue <- 2*pnorm(abs(WaldStatistic),lower.tail=FALSE)
  }
  colnames(WaldPvalue) <- paste0("WaldPvalue_",modelMatrixNames)
  
  betaConv <- fit$betaConv

  if (any(!betaConv)) {
    if (!quiet) message(paste(sum(!betaConv),"rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest"))
  }

  mleBetas <- if (betaPrior) {
    matrixToList(mleBetaMatrix)
  } else {
    NULL
  }

  # if useT need to add the t degrees of freedom to the end of resultsList
  tDFList <- if (useT) list(tDegreesFreedom=df) else NULL
  
  resultsList <- c(matrixToList(betaMatrix),
                   matrixToList(betaSE),
                   mleBetas,
                   matrixToList(WaldStatistic),
                   matrixToList(WaldPvalue),
                   list(betaConv = betaConv,
                        betaIter = fit$betaIter,
                        deviance = -2 * fit$logLike,
                        maxCooks = maxCooks),
                   tDFList)
  
  WaldResults <- buildDataFrameWithNARows(resultsList, mcols(object)$allZero)
  
  modelMatrixNamesSpaces <- gsub("_"," ",modelMatrixNames)

  lfcType <- if (attr(object,"betaPrior")) "MAP" else "MLE"
  coefInfo <- paste(paste0("log2 fold change (",lfcType,"):"),modelMatrixNamesSpaces)
  seInfo <- paste("standard error:",modelMatrixNamesSpaces)
  mleInfo <- if (betaPrior) {
    gsub("_"," ",colnames(mleBetaMatrix))
  } else {
    NULL
  }
  statInfo <- paste("Wald statistic:",modelMatrixNamesSpaces)
  pvalInfo <- paste("Wald test p-value:",modelMatrixNamesSpaces)

  tDFDescription <- if (useT) "t degrees of freedom for Wald test" else NULL  
  mcolsWaldResults <- DataFrame(type = rep("results",ncol(WaldResults)),
                                  description = c(coefInfo, seInfo, mleInfo, statInfo, pvalInfo,
                                    "convergence of betas",
                                    "iterations for betas",
                                    "deviance for the fitted model",
                                    "maximum Cook's distance for row",
                                    tDFDescription))
  
  mcols(WaldResults) <- mcolsWaldResults
 
  mcols(object) <- cbind(mcols(object),WaldResults)
  return(object)
}



#' Steps for estimating the beta prior variance
#'
#' These lower-level functions are called within \code{\link{DESeq}} or \code{\link{nbinomWaldTest}}.
#' End users should use those higher-level function instead.
#' NOTE: \code{estimateBetaPriorVar} returns a numeric vector, not a DESEqDataSet!
#' For advanced users: to use these functions, first run \code{estimateMLEForBetaPriorVar}
#' and then run \code{estimateBetaPriorVar}.
#'
#' @param object a DESeqDataSet
#'
#' @param maxit as defined in \code{link{nbinomWaldTest}}
#' @param useOptim as defined in \code{link{nbinomWaldTest}}
#' @param useQR as defined in \code{link{nbinomWaldTest}}
#' @param modelMatrixType an optional override for the type which is set internally
#' 
#' @param betaPriorMethod the method for calculating the beta prior variance,
#' either "quanitle" or "weighted":
#' "quantile" matches a normal distribution using the upper quantile of the finite MLE betas.
#' "weighted" matches a normal distribution using the upper quantile, but weighting by the variance of the MLE betas.
#' @param upperQuantile the upper quantile to be used for the
#' "quantile" or "weighted" method of beta prior variance estimation
#' @param modelMatrix an optional matrix, typically this is set to NULL
#' and created within the function
#' 
#' @return for \code{estimateMLEForBetaPriorVar}, a DESeqDataSet, with the
#' necessary information stored in order to calculate the prior variance.
#' for \code{estimateBetaPriorVar}, the vector of variances for the prior
#' on the betas in the \code{\link{DESeq}} GLM
#'
#' @aliases estimateBetaPriorVar estimateMLEForBetaPriorVar
#' 
#' @export
estimateBetaPriorVar <- function(object, 
                                 betaPriorMethod=c("weighted","quantile"),
                                 upperQuantile=0.05,
                                 modelMatrix=NULL) {
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]

  betaMatrix <- as.matrix(mcols(objectNZ)[,grep("MLE_", names(mcols(object))),drop=FALSE])
  colnamesBM <- colnames(betaMatrix)
  colnamesBM <- gsub("MLE_(.*)","\\1",colnamesBM)

  # renaming in reverse:
  # make these standard colnames as from model.matrix
  convertNames <- renameModelMatrixColumns(colData(object),design(object))
  colnamesBM <- sapply(colnamesBM, function(x) {
    if (x %in% convertNames$to) {
      convertNames$from[convertNames$to == x]
    } else {
      x
    }
  })
  colnames(betaMatrix) <- colnamesBM
  
  # this is the model matrix from an MLE run
  if (is.null(modelMatrix)) {
    modelMatrix <- getModelMatrix(object)
  }
  modelMatrixType <- attr(object, "modelMatrixType")
  
  betaPriorMethod <- match.arg(betaPriorMethod, choices=c("weighted","quantile"))

  # estimate the variance of the prior on betas
  # if expanded, first calculate LFC for all possible contrasts
  if (modelMatrixType == "expanded") {
    betaMatrix <- addAllContrasts(objectNZ, betaMatrix)
  }

  # weighting by 1/Var(log(K))
  # Var(log(K)) ~ Var(K)/mu^2 = 1/mu + alpha
  # and using the fitted alpha
  dispFit <- mcols(objectNZ)$dispFit
  if (is.null(dispFit)) {
    # betaPrior routine could have been called w/o the dispersion fitted trend
    dispFit <- mean(dispersions(objectNZ))
  }
  varlogk <- 1/mcols(objectNZ)$baseMean + dispFit
  weights <- 1/varlogk
  
  betaPriorVar <- if (nrow(betaMatrix) > 1) {
    apply(betaMatrix, 2, function(x) {
      # this test removes genes which have betas
      # tending to +/- infinity
      useFinite <- abs(x) < 10
      # if no more betas pass test, return wide prior
      if (sum(useFinite) == 0 ) {
        return(1e6)
      } else {
        if (betaPriorMethod=="quantile") {
          return(matchUpperQuantileForVariance(x[useFinite],upperQuantile))
        } else if (betaPriorMethod=="weighted") {
          return(matchWeightedUpperQuantileForVariance(x[useFinite],weights[useFinite],upperQuantile))
        }
      }
    })
  } else {
    (betaMatrix)^2
  }
  names(betaPriorVar) <- colnames(betaMatrix)
  
  # intercept set to wide prior
  if ("Intercept" %in% names(betaPriorVar)) {
    betaPriorVar[which(names(betaPriorVar) == "Intercept")] <- 1e6
  }

  # do the same for incoming model matrices
  # where intercept may be named "(Intercept)" via model.matrix
  if ("(Intercept)" %in% names(betaPriorVar)) {
    betaPriorVar[which(names(betaPriorVar) == "(Intercept)")] <- 1e6
  }
  
  if (modelMatrixType == "expanded") {
    # bring over beta priors from the GLM fit without prior.
    # for factors: prior variance of each level are the average of the
    # prior variances for the levels present in the previous GLM fit
    betaPriorExpanded <- averagePriorsOverLevels(objectNZ, betaPriorVar)
    betaPriorVar <- betaPriorExpanded
  }
  
  betaPriorVar
}

#' @rdname estimateBetaPriorVar
#' @export
estimateMLEForBetaPriorVar <- function(object, maxit=100, useOptim=TRUE, useQR=TRUE,
                                       modelMatrixType=NULL) {
  # this function copies code from other functions,
  # in order to allow parallelization  
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]

  if (is.null(modelMatrixType)) {
    # this code copied from nbinomWaldTest()
    blindDesign <- design(object) == formula(~ 1)
    mmTypeTest <- !blindDesign
    modelMatrixType <- if (mmTypeTest) {
                         "expanded"
                       } else {
                         "standard"
                       }
  }
  attr(object, "modelMatrixType") <- modelMatrixType

  # this code copied from fitGLMsWithPrior()
  fit <- fitNbinomGLMs(objectNZ, maxit=maxit, useOptim=useOptim, useQR=useQR,
                       renameCols = (modelMatrixType == "standard"))
  modelMatrix <- fit$modelMatrix
  modelMatrixNames <- colnames(modelMatrix)
  H <- fit$hat_diagonal
  betaMatrix <- fit$betaMatrix
 
  modelMatrixNames[modelMatrixNames == "(Intercept)"] <- "Intercept"
  modelMatrixNames <- make.names(modelMatrixNames)
  colnames(betaMatrix) <- modelMatrixNames
  
  convertNames <- renameModelMatrixColumns(colData(object),
                                           design(objectNZ))
  convertNames <- convertNames[convertNames$from %in% modelMatrixNames,,drop=FALSE]
  modelMatrixNames[match(convertNames$from, modelMatrixNames)] <- convertNames$to
  mleBetaMatrix <- fit$betaMatrix
  colnames(mleBetaMatrix) <- paste0("MLE_",modelMatrixNames)
  # remove any MLE columns if they exist
  mcols(object) <- mcols(object)[,grep("MLE_",names(mcols(object)),invert=TRUE)]
  mcols(object) <- cbind(mcols(object), buildDataFrameWithNARows(DataFrame(mleBetaMatrix), mcols(object)$allZero))
  assays(object, withDimnames=FALSE)[["H"]] <- buildMatrixWithNARows(H, mcols(object)$allZero)
  object
}

#' Likelihood ratio test (chi-squared test) for GLMs
#'
#' This function tests for significance of change in deviance between a
#' full and reduced model which are provided as \code{formula}.
#' Fitting uses previously calculated \code{\link{sizeFactors}} (or \code{\link{normalizationFactors}})
#' and dispersion estimates.
#' 
#' The difference in deviance is compared to a chi-squared distribution
#' with df = (reduced residual degrees of freedom - full residual degrees of freedom).
#' This function is comparable to the \code{nbinomGLMTest} of the previous version of DESeq
#' and an alternative to the default \code{\link{nbinomWaldTest}}.
#'
#' @param object a DESeqDataSet
#' @param full the full model formula, this should be the formula in
#' \code{design(object)}.
#' alternatively, can be a matrix
#' @param reduced a reduced formula to compare against, e.g.
#' the full model with a term or terms of interest removed.
#' alternatively, can be a matrix
#' @param betaTol control parameter defining convergence
#' @param maxit the maximum number of iterations to allow for convergence of the
#' coefficient vector
#' @param useOptim whether to use the native optim function on rows which do not
#' converge within maxit
#' @param quiet whether to print messages at each step
#' @param useQR whether to use the QR decomposition on the design
#' matrix X while fitting the GLM
#' @param minmu lower bound on the estimated count while fitting the GLM
#' @param type either "DESeq2" or "glmGamPoi". If \code{type = "DESeq2"} a
#' classical likelihood ratio test based on the Chi-squared distribution is
#' conducted. If \code{type = "glmGamPoi"} and previously the dispersion has
#' been estimated with glmGamPoi as well, a quasi-likelihood ratio test based
#' on the F-distribution is conducted. It is supposed to be more accurate, because
#' it takes the uncertainty of dispersion estimate into account in the same way
#' that a t-test improves upon a Z-test.
#' 
#' @return a DESeqDataSet with new results columns accessible
#' with the \code{\link{results}} function.  The coefficients and standard errors are
#' reported on a log2 scale.
#' 
#' @seealso \code{\link{DESeq}}, \code{\link{nbinomWaldTest}}
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' dds <- estimateSizeFactors(dds)
#' dds <- estimateDispersions(dds)
#' dds <- nbinomLRT(dds, reduced = ~ 1)
#' res <- results(dds)
#'
#' @export
nbinomLRT <- function(object, full=design(object), reduced,
                      betaTol=1e-8, maxit=100, useOptim=TRUE, quiet=FALSE,
                      useQR=TRUE,
                      minmu=if (type=="glmGamPoi") 1e-6 else 0.5,
                      type = c("DESeq2", "glmGamPoi")) {
  
  type <- match.arg(type, c("DESeq2", "glmGamPoi"))
  if (is.null(dispersions(object))) {
    stop("testing requires dispersion estimates, first call estimateDispersions()")
  }
  if (missing(reduced)) {
    stop("provide a reduced formula for the LRT, e.g. nbinomLRT(object, reduced= ~1)")
  }

  # in case the class of the mcols(mcols(object)) are not character
  object <- sanitizeRowRanges(object)
  
  # run check on the formula
  modelAsFormula <- !(is.matrix(full) & is.matrix(reduced))
  if (modelAsFormula) {
    checkLRT(full, reduced)

    # run some tests common to DESeq, nbinomWaldTest, nbinomLRT
    designAndArgChecker(object, betaPrior=FALSE)
    
    # try to form model matrices, test for difference
    # in residual degrees of freedom
    fullModelMatrix <- stats::model.matrix.default(full, data=as.data.frame(colData(object)))
    reducedModelMatrix <- stats::model.matrix.default(reduced, data=as.data.frame(colData(object)))
    df <- ncol(fullModelMatrix) - ncol(reducedModelMatrix)
  } else {
    df <- ncol(full) - ncol(reduced)
  }
  
  if (df < 1) stop("less than one degree of freedom, perhaps full and reduced models are not in the correct order")
  
  if (any(mcols(mcols(object))$type == "results")) {
    if (!quiet) message("found results columns, replacing these")
    object <- removeResults(object)
  } 

  if (is.null(mcols(object)$allZero)) {
    object <- getBaseMeansAndVariances(object)
  }
  
  if (modelAsFormula) {
    modelMatrixType <- "standard"
    # check for intercept
    hasIntercept <- attr(terms(design(object)),"intercept") == 1
    renameCols <- hasIntercept
  } else {
    modelMatrixType <- "user-supplied"
    renameCols <- FALSE
  }

  # store modelMatrixType
  attr(object,"modelMatrixType") <- modelMatrixType

  # only continue on the rows with non-zero row mean
  objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]

  if (type == "DESeq2") {
    if (modelAsFormula) {
      fullModel <- fitNbinomGLMs(objectNZ, modelFormula=full,
                                 renameCols=renameCols,
                                 betaTol=betaTol, maxit=maxit,
                                 useOptim=useOptim, useQR=useQR,
                                 warnNonposVar=FALSE, minmu=minmu)
      modelMatrix <- fullModel$modelMatrix
      reducedModel <- fitNbinomGLMs(objectNZ, modelFormula=reduced,
                                    betaTol=betaTol, maxit=maxit,
                                    useOptim=useOptim, useQR=useQR,
                                    warnNonposVar=FALSE, minmu=minmu)
      reducedModelMatrix <- reducedModel$modelMatrix
    } else {
      fullModel <- fitNbinomGLMs(objectNZ, modelMatrix=full,
                                 renameCols=FALSE,
                                 betaTol=betaTol, maxit=maxit,
                                 useOptim=useOptim, useQR=useQR,
                                 warnNonposVar=FALSE, minmu=minmu)
      modelMatrix <- full
      reducedModel <- fitNbinomGLMs(objectNZ, modelMatrix=reduced,
                                    renameCols=FALSE,
                                    betaTol=betaTol, maxit=maxit,
                                    useOptim=useOptim, useQR=useQR,
                                    warnNonposVar=FALSE, minmu=minmu)
      reducedModelMatrix <- reduced
    }
    
    # calculate LRT statistic and p-values
    LRTStatistic <- (2 * (fullModel$logLike - reducedModel$logLike))
    LRTPvalue <- pchisq(LRTStatistic, df=df, lower.tail=FALSE)
    
    deviance <- -2 * fullModel$logLike
    
    ### Handle Hat matrix and Cook distances
    H <- fullModel$hat_diagonals
    
    # calculate Cook's distance
    dispModelMatrix <- modelMatrix
    attr(object,"dispModelMatrix") <- dispModelMatrix
    cooks <- calculateCooksDistance(objectNZ, H, dispModelMatrix)
    
    # record maximum of Cook's
    maxCooks <- recordMaxCooks(design(object), colData(object), dispModelMatrix, cooks, nrow(objectNZ))
    
    # store hat matrix diagonals
    assays(object, withDimnames=FALSE)[["H"]] <- buildMatrixWithNARows(H, mcols(object)$allZero)
    
    # store Cook's distance for each sample
    assays(object, withDimnames=FALSE)[["cooks"]] <- buildMatrixWithNARows(cooks, mcols(object)$allZero)
  } else if (type == "glmGamPoi") {

    disp_trend <- mcols(objectNZ)$dispFit
    
    # check for normalization factors, if missing use size factors
    if (is.null(normalizationFactors(objectNZ))) {
      sf <- sizeFactors(objectNZ)
      fit_full <- glmGamPoi::glm_gp(objectNZ, design = full,
                                    size_factors = sf, 
                                    overdispersion = disp_trend,
                                    overdispersion_shrinkage = FALSE)
    } else {
      offset <- log( normalizationFactors(objectNZ) )
      fit_full <- glmGamPoi::glm_gp(objectNZ, design = full,
                                    size_factors = FALSE, offset = offset,
                                    overdispersion = disp_trend,
                                    overdispersion_shrinkage = FALSE)
    }

    # Get the stuff from objectNZ that is saved there by estimateDispersionMAP()
    fit_full$overdispersion_shrinkage_list <- list(ql_df0 = attr(object, "quasiLikelihood_df0"),
                                                   ql_disp_shrunken = mcols(objectNZ)$qlDispMAP,
                                                   dispersion_trend = mcols(objectNZ)$dispFit)
    if (any(vapply(fit_full$overdispersion_shrinkage_list, is.null, FUN.VALUE = FALSE))) {
      stop("nbinomLRT of type 'glmGamPoi' called, but one or more of 'attr(object, \"quasiLikelihood_df0\")', ",
           "'mcols(object)$qlDispMAP', or 'mcols(object)$dispFit' was null.\n",
           "Please call 'estimateDispersions(dds, fitType = \"glmGamPoi\")' before you call 'nbinomLRT' with ",
           "type \"glmGamPoi\"")
    }
    qlr <- glmGamPoi::test_de(fit_full, reduced = reduced, verbose = ! quiet)
    
    LRTStatistic <- qlr$f_statistic
    LRTPvalue <- qlr$pval
    
    modelMatrix <- fit_full$model_matrix
    reducedModelMatrix <- if (is.matrix(reduced)) {
      reduced
    } else {
      stats::model.matrix.default(reduced, data=as.data.frame(colData(objectNZ)))
    }
    
    fullModel <- list(betaMatrix = fit_full$Beta / log(2), # Make sure Beta are on log2-scale
                      betaSE = array(NA, dim(fit_full$Beta), dimnames = list(rownames(fit_full$Beta), paste0("SE_",colnames(fit_full$Beta)))),
                      mu = fit_full$Mu, betaConv = rep(TRUE, nrow(objectNZ)), betaIter = rep(NA, nrow(objectNZ)))
    reducedModel <- list(betaConv = rep(TRUE, nrow(objectNZ)))
    deviance <- fit_full$deviances
    maxCooks <- rep(NA, nrow(objectNZ))
    dispModelMatrix <- modelMatrix
    attr(object,"dispModelMatrix") <- dispModelMatrix
  }
  
  betaPriorVar <- rep(1e6, ncol(modelMatrix))
    
  attr(object,"betaPrior") <- FALSE
  attr(object,"betaPriorVar") <- betaPriorVar
  attr(object,"modelMatrix") <- modelMatrix
  attr(object,"reducedModelMatrix") <- reducedModelMatrix
  attr(object,"test") <- "LRT"

  # store mu in case the user did not call estimateDispersionsGeneEst
  dimnames(fullModel$mu) <- NULL
  assays(objectNZ, withDimnames=FALSE)[["mu"]] <- fullModel$mu
  assays(object, withDimnames=FALSE)[["mu"]] <- buildMatrixWithNARows(fullModel$mu, mcols(object)$allZero)

  
  if (any(!fullModel$betaConv)) {
    if (!quiet) message(paste(sum(!fullModel$betaConv),"rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT"))
  }

 
  
  # no need to store additional betas (no beta prior)
  mleBetas <- NULL
  
  # continue storing LRT results
  resultsList <- c(matrixToList(fullModel$betaMatrix),
                   matrixToList(fullModel$betaSE),
                   mleBetas,
                   list(LRTStatistic = LRTStatistic,
                        LRTPvalue = LRTPvalue,
                        fullBetaConv = fullModel$betaConv,
                        reducedBetaConv = reducedModel$betaConv,
                        betaIter = fullModel$betaIter,
                        deviance = deviance,
                        maxCooks = maxCooks))
  LRTResults <- buildDataFrameWithNARows(resultsList, mcols(object)$allZero)

  modelComparison <- if (modelAsFormula) {
    paste0("'",paste(as.character(full),collapse=" "),
           "' vs '", paste(as.character(reduced),collapse=" "),"'")
  } else {
    "full vs reduced"
  }

  modelMatrixNames <- colnames(fullModel$betaMatrix)
  modelMatrixNamesSpaces <- gsub("_"," ",modelMatrixNames)
  lfcType <- "MLE"
  coefInfo <- paste(paste0("log2 fold change (",lfcType,"):"),modelMatrixNamesSpaces)
  seInfo <- paste("standard error:",modelMatrixNamesSpaces)
  mleInfo <- NULL
  statInfo <- paste("LRT statistic:",modelComparison)
  pvalInfo <- paste("LRT p-value:",modelComparison)

  mcols(LRTResults) <- DataFrame(type = rep("results",ncol(LRTResults)),
                                 description = c(coefInfo, seInfo, mleInfo,
                                   statInfo, pvalInfo, 
                                   "convergence of betas for full model",
                                   "convergence of betas for reduced model",
                                   "iterations for betas for full model",
                                   "deviance of the full model",
                                   "maximum Cook's distance for row"))
  mcols(object) <- cbind(mcols(object),LRTResults)
  
  return(object)
}


#' Replace outliers with trimmed mean
#'
#' Note that this function is called within \code{\link{DESeq}}, so is not
#' necessary to call on top of a \code{DESeq} call. See the \code{minReplicatesForReplace}
#' argument documented in \code{link{DESeq}}.
#' 
#' This function replaces outlier counts flagged by extreme Cook's distances,
#' as calculated by \code{\link{DESeq}}, \code{\link{nbinomWaldTest}}
#' or \code{\link{nbinomLRT}}, with values predicted by the trimmed mean
#' over all samples (and adjusted by size factor or normalization factor).
#' This function replaces the counts in the matrix returned by \code{counts(dds)}
#' and the Cook's distances in \code{assays(dds)[["cooks"]]}. Original counts are
#' preserved in \code{assays(dds)[["originalCounts"]]}.
#' 
#' The \code{\link{DESeq}} function calculates a diagnostic measure called
#' Cook's distance for every gene and every sample. The \code{\link{results}}
#' function then sets the p-values to \code{NA} for genes which contain
#' an outlying count as defined by a Cook's distance above a threshold.
#' With many degrees of freedom, i.e. many more samples than number of parameters to 
#' be estimated-- it might be undesirable to remove entire genes from the analysis
#' just because their data include a single count outlier.
#' An alternate strategy is to replace the outlier counts
#' with the trimmed mean over all samples, adjusted by the size factor or normalization
#' factor for that sample. The following simple function performs this replacement
#' for the user, for samples which have at least \code{minReplicates} number
#' of replicates (including that sample).
#' For more information on Cook's distance, please see the two
#' sections of the vignette: 'Dealing with count outliers' and 'Count outlier detection'.
#' 
#' @param object a DESeqDataSet object, which has already been processed by
#' either DESeq, nbinomWaldTest or nbinomLRT, and therefore contains a matrix
#' contained in \code{assays(dds)[["cooks"]]}. These are the Cook's distances which will
#' be used to define outlier counts.
#' @param trim the fraction (0 to 0.5) of observations to be trimmed from
#' each end of the normalized counts for a gene before the mean is computed
#' @param cooksCutoff the threshold for defining an outlier to be replaced.
#' Defaults to the .99 quantile of the F(p, m - p) distribution, where p is
#' the number of parameters and m is the number of samples.
#' @param minReplicates the minimum number of replicate samples necessary to consider
#' a sample eligible for replacement (including itself). Outlier counts will not be replaced
#' if the sample is in a cell which has less than minReplicates replicates.
#' @param whichSamples optional, a numeric or logical index to specify
#' which samples should have outliers replaced. if missing, this is determined using
#' minReplicates.
#'
#' @seealso \code{\link{DESeq}}
#'
#' @aliases replaceOutliersWithTrimmedMean
#' 
#' @return a DESeqDataSet with replaced counts in the slot returned by
#' \code{\link{counts}} and the original counts preserved in
#' \code{assays(dds)[["originalCounts"]]}
#' 
#' @export
replaceOutliers <- function(object, trim=.2, cooksCutoff, minReplicates=7, whichSamples) {
  if (is.null(attr(object,"modelMatrix")) | !("cooks" %in% assayNames(object))) {
    stop("first run DESeq, nbinomWaldTest, or nbinomLRT to identify outliers")
  }
  if (minReplicates < 3) {
    stop("at least 3 replicates are necessary in order to indentify a sample as a count outlier")
  }
  stopifnot(is.numeric(minReplicates) & length(minReplicates) == 1)
  p <- ncol(attr(object,"modelMatrix"))
  m <- ncol(object)
  if (m <= p) {
    assays(object, withDimnames=FALSE)[["originalCounts"]] <- counts(object)
    return(object)
  }
  if (missing(cooksCutoff)) {
    cooksCutoff <- qf(.99, p, m - p)
  }
  idx <- which(assays(object)[["cooks"]] > cooksCutoff)
  mcols(object)$replace <- apply(assays(object)[["cooks"]], 1, function(row) any(row > cooksCutoff))
  mcols(mcols(object),use.names=TRUE)["replace",] <- DataFrame(type="intermediate",description="had counts replaced")
  trimBaseMean <- apply(counts(object,normalized=TRUE),1,mean,trim=trim)
  # build a matrix of counts based on the trimmed mean and the size factors
  replacementCounts <- if (!is.null(normalizationFactors(object))) {
    as.integer(matrix(rep(trimBaseMean,ncol(object)),ncol=ncol(object)) * 
               normalizationFactors(object))
  } else {
    as.integer(outer(trimBaseMean, sizeFactors(object), "*"))
  }
  
  # replace only those values which fall above the cutoff on Cook's distance
  newCounts <- counts(object)
  newCounts[idx] <- replacementCounts[idx]
  
  if (missing(whichSamples)) {
    whichSamples <- nOrMoreInCell(attr(object,"modelMatrix"), n = minReplicates)
  }
  stopifnot(is.logical(whichSamples))
  object$replaceable <- whichSamples
  mcols(colData(object),use.names=TRUE)["replaceable",] <- DataFrame(type="intermediate",
                         description="outliers can be replaced")
  assays(object, withDimnames=FALSE)[["originalCounts"]] <- counts(object)
  if (sum(whichSamples) == 0) {
    return(object)
  }
  counts(object)[,whichSamples] <- newCounts[,whichSamples,drop=FALSE]
  object
}

#' @export
#' @rdname replaceOutliers
replaceOutliersWithTrimmedMean <- replaceOutliers


###########################################################
# unexported functons 
###########################################################


# Get base means and variances
#
# An internally used function to calculate the row means and variances
# from the normalized counts, which requires that \code{\link{estimateSizeFactors}}
# has already been called.  Adds these and a logical column if the row sums
# are zero to the mcols of the object.
#
# object a DESeqDataSet object
#
# return a DESeqDataSet object with columns baseMean
# and baseVar in the row metadata columns
getBaseMeansAndVariances <- function(object) {
  cts.norm <- counts(object,normalized=TRUE)
  if ("weights" %in% assayNames(object)) {
    wts <- assays(object)[["weights"]]
    cts.norm <- wts * cts.norm
  }
  meanVarZero <- DataFrame(baseMean = unname(rowMeans(cts.norm)),
                           baseVar = unname(rowVars(cts.norm)),
                           allZero = unname(rowSums(counts(object)) == 0))
  mcols(meanVarZero) <- DataFrame(type = rep("intermediate",ncol(meanVarZero)),
                                  description = c("mean of normalized counts for all samples",
                                    "variance of normalized counts for all samples",
                                    "all counts for a gene are zero"))
  if (all(c("baseMean","baseVar","allZero") %in% names(mcols(object)))) {
      mcols(object)[c("baseMean","baseVar","allZero")] <- meanVarZero
  } else {
      mcols(object) <- cbind(mcols(object),meanVarZero)
  }
  return(object)
}

estimateNormFactors <- function(counts, normMatrix, locfunc=median, geoMeans, controlGenes) {
  sf <- estimateSizeFactorsForMatrix(counts / normMatrix, locfunc=locfunc, geoMeans=geoMeans, controlGenes=controlGenes)
  nf <- t( t(normMatrix) * sf )
  nf / exp(rowMeans(log(nf)))
}

# Estimate a parametric fit of dispersion to the mean intensity
parametricDispersionFit <- function( means, disps ) {
   coefs <- c( .1, 1 )
   iter <- 0
   while(TRUE) {
      residuals <- disps / ( coefs[1] + coefs[2] / means )
      good <- which( (residuals > 1e-4) & (residuals < 15) )
      # check for glm convergence below to exit while-loop
      suppressWarnings({fit <- glm( disps[good] ~ I(1/means[good]),
         family=Gamma(link="identity"), start=coefs )})
      oldcoefs <- coefs
      coefs <- coefficients(fit)
      if ( !all( coefs > 0 ) )
         stop(simpleError("parametric dispersion fit failed"))
      if ( ( sum( log( coefs / oldcoefs )^2 ) < 1e-6 )  & fit$converged )
         break
      iter <- iter + 1
      if ( iter > 10 ) 
        stop(simpleError("dispersion fit did not converge"))
    }
   names( coefs ) <- c( "asymptDisp", "extraPois" )
   ans <- function(q) coefs[1] + coefs[2] / q
   attr( ans, "coefficients" ) <- coefs
   ans
}


# Local fit of dispersion to the mean intensity
# fitting is done on log dispersion, log mean scale
localDispersionFit <- function( means, disps, minDisp ) {
  if (all(disps < minDisp*10)) {
    return(rep(minDisp,length(disps)))
  }
  d <- data.frame(logDisps = log(disps), logMeans = log(means))
  fit <- locfit(logDisps ~ logMeans, data=d[disps >= minDisp*10,,drop=FALSE],
                weights = means[disps >= minDisp*10])
  dispFunction <- function(means) exp(predict(fit, data.frame(logMeans=log(means))))
  return(dispFunction)
}


# convenience function for testing the log likelihood
# for a count matrix, mu matrix and vector disp
nbinomLogLike <- function(counts, mu, disp, weights, useWeights) {
  if (is.null(disp)) return(NULL)
  if (useWeights) {
    rowSums(weights * matrix(dnbinom(counts,mu=mu,size=1/disp,
                           log=TRUE),ncol=ncol(counts)))
  } else {
    rowSums(matrix(dnbinom(counts,mu=mu,size=1/disp,
                           log=TRUE),ncol=ncol(counts)))    
  }
}

# simple function to return a matrix of size factors
# or normalization factors
getSizeOrNormFactors <- function(object) {
  if (!is.null(normalizationFactors(object))) {
    return(normalizationFactors(object))
  } else { 
    return(matrix(rep(sizeFactors(object),each=nrow(object)),
             ncol=ncol(object)))
  }
}

# convenience function for building results tables
# out of a list and filling in NA rows
buildDataFrameWithNARows <- function(resultsList, NArows) {
  lengths <- sapply(resultsList,length)
  if (!all(lengths == lengths[1])) {
    stop("lengths of vectors in resultsList must be equal")
  }
  if (sum(!NArows) != lengths[1]) {
    stop("number of non-NA rows must be equal to lengths of vectors in resultsList")
  }
  if (sum(NArows) == 0) {
    return(DataFrame(resultsList))
  }
  dfFull <- DataFrame(lapply(resultsList, function(x) vector(mode(x), length(NArows))))
  dfFull[NArows,] <- NA
  dfFull[!NArows,] <- DataFrame(resultsList)
  dfFull
}

# convenience function for building larger matrices
# by filling in NA rows
buildMatrixWithNARows <- function(m, NARows) {
  mFull <- matrix(NA, ncol=ncol(m), nrow=length(NARows))
  mFull[!NARows,] <- m
  mFull
}

# convenience function for building larger matrices
# by filling in 0 rows
buildMatrixWithZeroRows <- function(m, zeroRows) {
  mFull <- matrix(0, ncol=ncol(m), nrow=length(zeroRows))
  mFull[!zeroRows,] <- m
  mFull
}

# convenience function for breaking up matrices
# by column and preserving column names
matrixToList <- function(m) {
  l <- split(m, col(m))
  names(l) <- colnames(m)
  l
}


# calculate a robust method of moments dispersion,
# in order to estimate the dispersion excluding
# individual outlier counts which would raise the variance estimate
robustMethodOfMomentsDisp <- function(object, modelMatrix) {
  cnts <- counts(object,normalized=TRUE)
  # if there are 3 or more replicates in any cell
  threeOrMore <- nOrMoreInCell(modelMatrix,n=3)
  v <- if (any(threeOrMore)) {
    cells <- apply(modelMatrix,1,paste0,collapse="")
    cells <- unname(factor(cells,levels=unique(cells)))
    levels(cells) <- seq_along(levels(cells))
    levelsThreeOrMore <- levels(cells)[table(cells) >= 3]
    idx <- cells %in% levelsThreeOrMore
    cntsSub <- cnts[,idx,drop=FALSE]
    cellsSub <- factor(cells[idx])
    trimmedCellVariance(cntsSub, cellsSub)
  } else {
    trimmedVariance(cnts)
  }
  m <- rowMeans(cnts)
  alpha <- ( v - m ) / m^2
  # cannot use the typical minDisp = 1e-8 here or else all counts in the same
  # group as the outlier count will get an extreme Cook's distance
  minDisp <- 0.04
  alpha <- pmax(alpha, minDisp)
  alpha
}

trimmedCellVariance <- function(cnts, cells) {
  # how much to trim at different n
  trimratio <- c(1/3, 1/4, 1/8)
  # returns an index for the vector above for three sample size bins
  trimfn <- function(n) as.integer(cut(n, breaks=c(0,3.5,23.5,Inf)))
  cellMeans <- matrix(sapply(levels(cells), function(lvl) {
    n <- sum(cells==lvl)
    apply(cnts[,cells==lvl,drop=FALSE],1,mean,trim=trimratio[trimfn(n)])
  }),
                      nrow=nrow(cnts))
  qmat <- cellMeans[,as.integer(cells),drop=FALSE]
  sqerror <- (cnts - qmat)^2
  varEst <- matrix(sapply(levels(cells), function(lvl) {
    n <- sum(cells==lvl)
    # scale due to trimming of large squares, by e.g. 1/mean(rnorm(1e6)^2,trim=1/8)
    scale.c <- c(2.04, 1.86, 1.51)[trimfn(n)]
    scale.c * apply(sqerror[,cells==lvl,drop=FALSE],1,mean,trim=trimratio[trimfn(n)])
  }),
                   nrow=nrow(sqerror))
  # take the max of variance estimates from cells
  # as one condition might have highly variable counts
  rowMax(varEst)
}

trimmedVariance <- function(x) {
  rm <-  apply(x,1,mean,trim=1/8)
  sqerror <- (x - rm)^2
  # scale due to trimming of large squares
  1.51 * apply(sqerror,1,mean,trim=1/8)
}

calculateCooksDistance <- function(object, H, modelMatrix) {
  p <- ncol(modelMatrix)
  dispersions <- robustMethodOfMomentsDisp(object, modelMatrix)
  V <- assays(object)[["mu"]] + dispersions * assays(object)[["mu"]]^2
  PearsonResSq <- (counts(object) - assays(object)[["mu"]])^2 / V
  cooks <- PearsonResSq / p  * H / (1 - H)^2
  cooks
}


# this function breaks out the logic for calculating the max Cook's distance:
# the samples over which max Cook's distance is calculated:
#
# Cook's distance is considered for those samples with 3 or more replicates per cell
#
# if m == p or there are no samples over which to calculate max Cook's, then give NA
recordMaxCooks <- function(design, colData, modelMatrix, cooks, numRow) {
    samplesForCooks <- nOrMoreInCell(modelMatrix, n=3)
    p <- ncol(modelMatrix)
    m <- nrow(modelMatrix)
    maxCooks <- if ((m > p) & any(samplesForCooks)) {
      apply(cooks[,samplesForCooks,drop=FALSE], 1, max)
    } else {
      rep(NA, numRow)
    }
    maxCooks
}

# for each sample in the model matrix,
# are there n or more replicates in the same cell
# (including that sample)
# so for a 2 x 3 comparison, the returned vector for n = 3 is:
# FALSE, FALSE, TRUE, TRUE, TRUE
nOrMoreInCell <- function(modelMatrix, n){
  row_hash <- apply(modelMatrix, 1, paste0, collapse = "_")
  hash_table <- table(row_hash)
  numEqual <- as.vector(unname(hash_table[row_hash]))
  numEqual >= n
}


# an unexported diagnostic function
# to retrieve the covariance matrix
# for the GLM coefficients of a single row
# only for standard model matrices
covarianceMatrix <- function(object, rowNumber) {
  if (attr(object, "modelMatrixType") != "standard")
    stop("only for standard model matrices")
  # convert coefficients to log scale
  coefColumns <- names(mcols(object))[grep("log2 fold change",mcols(mcols(object))$description)]
  beta <- log(2) * as.numeric(as.data.frame(mcols(object)[rowNumber,coefColumns,drop=FALSE]))
  x <- getModelMatrix(object)
  y <- counts(object)[rowNumber,]
  sf <- sizeFactors(object)
  alpha <- dispersions(object)[rowNumber]
  mu.hat <- as.vector(sf * exp(x %*% beta))
  minmu <- 0.5
  mu.hat[mu.hat < minmu] <- minmu
  w <- diag(1/(1/mu.hat^2 * ( mu.hat + alpha * mu.hat^2 )))
  betaPriorVar <- attr(object,"betaPriorVar")
  ridge <- diag(1/(log(2)^2 * betaPriorVar))
  sigma <- solve(t(x) %*% w %*% x + ridge) %*% (t(x) %*% w %*% x) %*% t(solve(t(x) %*% w %*% x + ridge))
  # convert back to log2 scale
  sigmaLog2Scale <- log2(exp(1))^2 * sigma
  sigmaLog2Scale
}

getDesignFactors <- function(object) {
  design <- design(object)
  designVars <- all.vars(design)
  designVarsClass <- sapply(designVars, function(v) class(colData(object)[[v]]))
  designVars[designVarsClass == "factor"]
}

# looking at the values of x which are large
# in absolute value, find the zero-centered Normal distribution
# with the matching quantile, and return the variance
# of that Normal distribution
matchUpperQuantileForVariance <- function(x, upperQuantile=.05) {
  sdEst <- quantile(abs(x), 1 - upperQuantile) / qnorm(1 - upperQuantile/2)
  unname(sdEst)^2
}

matchWeightedUpperQuantileForVariance <- function(x, weights, upperQuantile=.05) {
  sdEst <- Hmisc.wtd.quantile(abs(x), weights=weights, 1 - upperQuantile, normwt=TRUE) / qnorm(1 - upperQuantile/2)
  unname(sdEst)^2
}

# rough dispersion estimate using counts and fitted values
roughDispEstimate <- function(y, x) {

  # must be positive
  mu <- linearModelMu(y, x)
  mu <- matrix(pmax(1, mu), ncol=ncol(mu))
  
  m <- nrow(x)
  p <- ncol(x)

  # an alternate rough estimator with higher mean squared or absolute error
  # (rowSums( (y - mu)^2/(mu * (m - p)) ) - 1)/rowMeans(mu)
  
  # rough disp ests will be adjusted up to minDisp later
  est <- rowSums( ((y - mu)^2 - mu) / mu^2 ) / (m - p)
  pmax(est, 0)
}

momentsDispEstimate <- function(object) {
  xim <- if (!is.null(normalizationFactors(object))) {
    mean(1/colMeans(normalizationFactors(object)))
  } else {
    mean(1/sizeFactors(object))
  }
  bv <- mcols(object)$baseVar
  bm <- mcols(object)$baseMean
  (bv - xim*bm)/bm^2
}

modelMatrixGroups <- function(x) {
  factor(unname(apply(x, 1, paste0, collapse="__")))
}

linearModelMu <- function(y, x) {
  qrx <- qr(x)    
  Q <- qr.Q(qrx)  
  Rinv <- solve(qr.R(qrx))
  # old code:
  # hatmatrix <- x %*% Rinv %*% t(Q)
  # t(hatmatrix %*% t(y))
  # Wolfgang Huber's rewrite is up to 2 orders of magnitude faster (Sept 2018):
  (y %*% Q) %*% t(x %*% Rinv)
}

linearModelMuNormalized <- function(object, x) {
  cts <- counts(object)
  norm.cts <- counts(object, normalized=TRUE)
  muhat <- linearModelMu(norm.cts, x)
  nf <- getSizeOrNormFactors(object)
  muhat * nf
}

# checks for LRT formulas, written as function to remove duplicate code
# in DESeq and nbinomLRT
checkLRT <- function(full, reduced) {
  reducedNotInFull <- !all.vars(reduced) %in% all.vars(full)
  if (any(reducedNotInFull)) {
    stop(paste("the following variables in the reduced formula not in the full formula:",
               paste(all.vars(reduced)[reducedNotInFull],collapse=", ")))
  }
}

# bulky code separated from DESeq()
refitWithoutOutliers <- function(object, test, betaPrior, full, reduced,
                                 quiet, minReplicatesForReplace, modelMatrix, modelMatrixType) {
  cooks <- assays(object)[["cooks"]]
  object <- replaceOutliers(object, minReplicates=minReplicatesForReplace)

  # refit without outliers, if there were any replacements
  nrefit <- sum(mcols(object)$replace, na.rm=TRUE)
  if ( nrefit > 0 ) {
    object <- getBaseMeansAndVariances(object)
    newAllZero <- which(mcols(object)$replace & mcols(object)$allZero)
  }
  # only refit if some of the replacements don't result in all zero counts
  # otherwise, these cases are handled by results()
  if ( nrefit > 0 && nrefit > length(newAllZero) ) {
    if (!quiet) message(paste("-- replacing outliers and refitting for", nrefit,"genes
-- DESeq argument 'minReplicatesForReplace' =",minReplicatesForReplace,"
-- original counts are preserved in counts(dds)"))
    
    # refit on those rows which had replacement
    refitReplace <- which(mcols(object)$replace & !mcols(object)$allZero)
    objectSub <- object[refitReplace,]
    intermediateOrResults <- which(mcols(mcols(objectSub))$type %in% c("intermediate","results"))
    mcols(objectSub) <- mcols(objectSub)[,-intermediateOrResults,drop=FALSE]

    # estimate gene-wise dispersion
    if (!quiet) message("estimating dispersions")
    objectSub <- estimateDispersionsGeneEst(objectSub, quiet=quiet, modelMatrix=modelMatrix)
    
    # need to redo fitted dispersion due to changes in base mean
    mcols(objectSub)$dispFit <- dispersionFunction(objectSub)(mcols(objectSub)$baseMean)
    mcols(mcols(objectSub),use.names=TRUE)["dispFit",] <- DataFrame(type="intermediate",
                             description="fitted values of dispersion")
    dispPriorVar <- attr( dispersionFunction(object), "dispPriorVar" )

    # estimate dispersion MAP
    objectSub <- estimateDispersionsMAP(objectSub, quiet=quiet,
                                        dispPriorVar=dispPriorVar, modelMatrix=modelMatrix)

    # fit GLM
    if (!quiet) message("fitting model and testing")
    if (test == "Wald") {
      betaPriorVar <- attr(object, "betaPriorVar")
      objectSub <- nbinomWaldTest(objectSub, betaPrior=betaPrior,
                                  betaPriorVar=betaPriorVar, quiet=quiet,
                                  modelMatrix=modelMatrix,
                                  modelMatrixType=modelMatrixType)
    } else if (test == "LRT") {
      objectSub <- nbinomLRT(objectSub, full=full, reduced=reduced, quiet=quiet)
    }
    
    idx <- match(names(mcols(objectSub)), names(mcols(object)))
    mcols(object)[refitReplace, idx] <- mcols(objectSub)
    mcols(object)[newAllZero, mcols(mcols(object))$type == "results"] <- NA
    
    # continue to flag if some conditions have less than minReplicatesForReplace
    if (all(object$replaceable)) {
      mcols(object)$maxCooks <- NA
    } else {
      replaceCooks <- assays(object)[["cooks"]]
      replaceCooks[,object$replaceable] <- 0
      mcols(object)$maxCooks <- recordMaxCooks(design(object), colData(object),
                                               attr(object,"dispModelMatrix"), replaceCooks, nrow(object))
    }
  }
  
  if ( nrefit > 0 ) {
    # save the counts used for fitting as replaceCounts
    assays(object, withDimnames=FALSE)[["replaceCounts"]] <- counts(object)
    assays(object, withDimnames=FALSE)[["replaceCooks"]] <- assays(object)[["cooks"]]

    # preserve original counts and Cook's distances
    counts(object) <- assays(object)[["originalCounts"]]
    assays(object, withDimnames=FALSE)[["cooks"]] <- cooks
    
    # no longer need this assay slot
    assays(object)[["originalCounts"]] <- NULL
  }
  
  object
}

sanitizeRowRanges <- function(object) {
  if (is.null(mcols(mcols(object)))) {
    mcols(mcols(object)) <- DataFrame(type=rep("input",ncol(mcols(object))),
                                      description=character(ncol(mcols(object))))
  }
  class(mcols(mcols(object))$type) <- "character"
  class(mcols(mcols(object))$description) <- "character"
  mcols(mcols(object))$type[ is.na(mcols(mcols(object))$type) ] <- ""
  mcols(mcols(object))$description[ is.na(mcols(mcols(object))$description) ] <- ""
  object
}

sanitizeColData <- function(object) {
  if (is.null(mcols(colData(object)))) {
    mcols(colData(object)) <- DataFrame(type=rep("input",ncol(colData(object))),
                                        description=character(ncol(colData(object))))
  }
  class(mcols(colData(object))$type) <- "character"
  class(mcols(colData(object))$description) <- "character"
  mcols(colData(object))$type[ is.na(mcols(colData(object))$type) ] <- ""
  mcols(colData(object))$description[ is.na(mcols(colData(object))$description) ] <- ""
  object
}

estimateSizeFactorsIterate <- function(object, niter=10, Q=0.05) {
  design(object) <- ~ 1
  sf <- rep(1, ncol(object))
  idx <- rowSums(counts(object)) > 0
  cts <- counts(object)[idx,]
  for (i in seq_len(niter)) {
    sizeFactors(object) <- sf
    object <- estimateDispersions(object, fitType="mean", quiet=TRUE)
    q <- t(t(assays(object)[["mu"]])/sf)[idx,]
    disps <- dispersions(object)[idx]
    sf.old <- sf
    fn <- function(p) {
      sf <- exp(p - mean(p))
      mu.new <- t(t(q) * sf)
      ll <- matrix(dnbinom(cts, mu=mu.new, size=1/disps, log=TRUE), ncol=ncol(cts))
      gene.ll <- rowSums(ll)
      sum(gene.ll[ gene.ll > quantile(gene.ll, Q) ])
    }
    res <- optim(log(sf.old), fn, control=list(fnscale=-1), method="L-BFGS-B")
    if (res$convergence != 0) {
      stop("iterative size factor normalization did not converge within an iteration")
    }
    sf <- exp(res$par - mean(res$par))
    # loop more than once, and test for convergence
    if (i > 1 & sum((log(sf.old) - log(sf))^2) < 1e-4) {
      break
    } else {
      if (i == niter) {
        stop("iterative size factor normalization did not converge")
      }
    }
  }
  sf
}

checkFullRank <- function(modelMatrix) {
  if (qr(modelMatrix)$rank < ncol(modelMatrix)) {
    if (any(apply(modelMatrix, 2, function(col) all(col == 0)))) {
      stop("the model matrix is not full rank, so the model cannot be fit as specified.
  Levels or combinations of levels without any samples have resulted in
  column(s) of zeros in the model matrix.

  Please read the vignette section 'Model matrix not full rank':

  vignette('DESeq2')")
    } else {
      stop("the model matrix is not full rank, so the model cannot be fit as specified.
  One or more variables or interaction terms in the design formula are linear
  combinations of the others and must be removed.

  Please read the vignette section 'Model matrix not full rank':

  vignette('DESeq2')")
    }
  }
}

designAndArgChecker <- function(object, betaPrior) {
  termsOrder <- attr(terms.formula(design(object)),"order")
  hasIntercept <- attr(terms(design(object)),"intercept") == 1
  interactionPresent <- any(termsOrder > 1)
  if (betaPrior & !hasIntercept) {
    stop("betaPrior=TRUE can only be used if the design has an intercept.
  if specifying + 0 in the design formula, use betaPrior=FALSE")
  }
  if (betaPrior & interactionPresent) {
    stop("betaPrior=FALSE should be used for designs with interactions")
  }

  if (!betaPrior) {
    mm <- stats::model.matrix(design(object), data=as.data.frame(colData(object)))
    q <- qr(mm)
    if (q$rank < ncol(mm))
      stop("full model matrix is less than full rank")
  }
  
  design <- design(object)
  designVars <- all.vars(design)
  if (length(designVars) > 0) {
    if (any(sapply(designVars, function(v) any(is.na(colData(object)[[v]]))))) {
      stop("variables in the design formula cannot have NA values")
    }
    designFactors <- designVars[sapply(designVars, function(v) is(colData(object)[[v]], "factor"))]
    if (length(designFactors) > 0 && any(sapply(designFactors,function(v) any(table(colData(object)[[v]]) == 0)))) {
      stop("factors in design formula must have samples for each level.
  this error can arise when subsetting a DESeqDataSet, in which
  all the samples for one or more levels of a factor in the design were removed.
  if this was intentional, use droplevels() to remove these levels, e.g.:

  dds$condition <- droplevels(dds$condition)
")
    }
    if (any(sapply(designVars, function(v) is(colData(object)[[v]], "ordered")))) {
      stop("the design formula contains an ordered factor. The internal steps
do not work on ordered factors as a formula. Instead you should provide a matrix to
the 'design' slot or to the 'full' argument of DESeq(), constructed using model.matrix.")
    }
  }
}

getModelMatrix <- function(object) {
  if (is(design(object), "matrix")) {
    design(object)
  } else if (is(design(object), "formula")) {
    stats::model.matrix.default(design(object), data=as.data.frame(colData(object)))
  }
}

getAndCheckWeights <- function(object, modelMatrix, weightThreshold=1e-2) {
  if ("weights" %in% assayNames(object)) {
    useWeights <- TRUE
    weights <- unname(assays(object)[["weights"]])
    stopifnot(all(weights >= 0))
    weights <- weights / apply(weights, 1, max)
    # some code for testing whether still full rank
    # only performed once per analysis, by setting object attribute
    if (is.null(attr(object, "weightsOK"))) {
      m <- ncol(modelMatrix)
      full.rank <- qr(modelMatrix)$rank == m
      weights.ok <- logical(nrow(weights))
      # most designs are full rank with current version of DESeq2
      if (full.rank) {
        for (i in seq_len(nrow(weights))) {
          # note: downweighting of samples very low will still be full rank
          # so this test is kind of minimally in play -- good for checking
          # the user input however, e.g. all zero weights for a gene
          test1 <- qr(weights[i,] * modelMatrix)$rank == m
          # we test that it will be possible to calculate the CR term
          # following subsetting based on weightThreshold
          mm.sub <- modelMatrix[weights[i,] > weightThreshold,,drop=FALSE]
          mm.sub <- mm.sub[,colSums(abs(mm.sub)) > 0,drop=FALSE]
          test2 <- qr(mm.sub)$rank == ncol(mm.sub)
          weights.ok[i] <- test1 & test2
        }
      } else {
        # model matrix is not full rank (backwards compatibility for betaPrior=TRUE)
        # just check zero columns
        weights.ok <- rep(TRUE, nrow(weights))
        for (j in seq_len(ncol(modelMatrix))) {
          num.zero <- colSums(t(weights) * modelMatrix[,j] == 0)
          weights.ok <- weights.ok & (num.zero != nrow(modelMatrix))
        }
      }
      # instead of giving an error, switch allZero to TRUE for the problem rows
      if (!all(weights.ok)) {
        mcols(object)$allZero[!weights.ok] <- TRUE
        weightsDF <- DataFrame(weightsFail = !weights.ok)
        mcols(weightsDF) <- DataFrame(type="intermediate",
                                      description="weights fail to allow parameter estimation")
        mcols(object) <- cbind(mcols(object), weightsDF)
        warning(paste("for", sum(!weights.ok),
  "row(s), the weights as supplied won't allow parameter estimation, producing a
  degenerate design matrix. These rows have been flagged in mcols(dds)$weightsFail
  and treated as if the row contained all zeros (mcols(dds)$allZero set to TRUE).
  If you are blocking for donors/organisms, consider design = ~0+donor+condition."))
      }
    }
    attr(object, "weightsOK") <- TRUE
  } else {
    useWeights <- FALSE
    weights <- matrix(1, nrow=nrow(object), ncol=ncol(object))
  }
  list(object=object,weights=weights,useWeights=useWeights)
}

#################################################
## functions from Hmisc for Hmisc.wtd.quantile ##
#################################################

# this and the following two functions are copied from Hmisc
# to avoid extra package dependencies in DESeq2 (same license as Hmisc),
# with the alteration of commenting out `isdate` test
# https://cran.r-project.org/package=Hmisc
Hmisc.wtd.quantile <- function(x, weights=NULL, probs=c(0, .25, .5, .75, 1), 
                         type=c('quantile','(i-1)/(n-1)','i/(n+1)','i/n'), 
                         normwt=FALSE, na.rm=TRUE)
{
  if(! length(weights))
    return(quantile(x, probs=probs, na.rm=na.rm))

  type <- match.arg(type)
  if(any(probs < 0 | probs > 1))
    stop("Probabilities must be between 0 and 1 inclusive")

  nams <- paste(format(round(probs * 100, if(length(probs) > 1) 
                             2 - log10(diff(range(probs))) else 2)), 
                "%", sep = "")

  i <- is.na(weights) | weights == 0
  if(any(i)) {
    x <- x[! i]
    weights <- weights[! i]
    }
  if(type == 'quantile') {
    w <- Hmisc.wtd.table(x, weights, na.rm=na.rm, normwt=normwt, type='list')
    x     <- w$x
    wts   <- w$sum.of.weights
    n     <- sum(wts)
    order <- 1 + (n - 1) * probs
    low   <- pmax(floor(order), 1)
    high  <- pmin(low + 1, n)
    order <- order %% 1
    ## Find low and high order statistics
    ## These are minimum values of x such that the cum. freqs >= c(low,high)
    allq <- approx(cumsum(wts), x, xout=c(low,high), 
                   method='constant', f=1, rule=2)$y
    k <- length(probs)
    quantiles <- (1 - order)*allq[1:k] + order*allq[-(1:k)]
    names(quantiles) <- nams
    return(quantiles)
  } 
  w <- Hmisc.wtd.Ecdf(x, weights, na.rm=na.rm, type=type, normwt=normwt)
  structure(approx(w$ecdf, w$x, xout=probs, rule=2)$y, 
            names=nams)
}


Hmisc.wtd.Ecdf <- function(x, weights=NULL, 
                     type=c('i/n','(i-1)/(n-1)','i/(n+1)'), 
                     normwt=FALSE, na.rm=TRUE)
{
  type <- match.arg(type)
  switch(type,
         '(i-1)/(n-1)'={a <- b <- -1},
         'i/(n+1)'    ={a <- 0; b <- 1},
         'i/n'        ={a <- b <- 0})

  if(! length(weights)) {
    ##.Options$digits <- 7  ## to get good resolution for names(table(x))
    oldopt <- options('digits')
    options(digits=7)
    on.exit(options(oldopt))
    cumu <- table(x)    ## R does not give names for cumsum
    #isdate <- testDateTime(x)  ## 31aug02
    ax <- attributes(x)
    ax$names <- NULL
    x <- as.numeric(names(cumu))
    #if(isdate) attributes(x) <- c(attributes(x),ax)
    cumu <- cumsum(cumu)
    cdf <- (cumu + a)/(cumu[length(cumu)] + b)
    if(cdf[1]>0) {
      x <- c(x[1], x);
      cdf <- c(0,cdf)
    }

    return(list(x = x, ecdf=cdf))
  }

  w <- Hmisc.wtd.table(x, weights, normwt=normwt, na.rm=na.rm)
  cumu <- cumsum(w$sum.of.weights)
  cdf <- (cumu + a)/(cumu[length(cumu)] + b)
  list(x = c(if(cdf[1]>0) w$x[1], w$x), ecdf=c(if(cdf[1]>0)0, cdf))
}


Hmisc.wtd.table <- function(x, weights=NULL, type=c('list','table'), 
                      normwt=FALSE, na.rm=TRUE)
{
  type <- match.arg(type)
  if(! length(weights))
    weights <- rep(1, length(x))

  #isdate <- testDateTime(x)  ## 31aug02 + next 2
  ax <- attributes(x)
  ax$names <- NULL
  
  if(is.character(x)) x <- as.factor(x)
  lev <- levels(x)
  x <- unclass(x)
  
  if(na.rm) {
    s <- ! is.na(x + weights)
    x <- x[s, drop=FALSE]    ## drop is for factor class
    weights <- weights[s]
  }

  n <- length(x)
  if(normwt)
    weights <- weights * length(x) / sum(weights)

  i <- order(x)  # R does not preserve levels here
  x <- x[i]; weights <- weights[i]

  if(anyDuplicated(x)) {  ## diff(x) == 0 faster but doesn't handle Inf
    weights <- tapply(weights, x, sum)
    if(length(lev)) {
      levused <- lev[sort(unique(x))]
      if((length(weights) > length(levused)) &&
         any(is.na(weights)))
        weights <- weights[! is.na(weights)]

      if(length(weights) != length(levused))
        stop('program logic error')

      names(weights) <- levused
    }

    if(! length(names(weights)))
      stop('program logic error')

    if(type=='table')
      return(weights)

    # modified from Hmisc::all.is.numeric
    x <- as.numeric(names(weights))
    #if(isdate)
    #  attributes(x) <- c(attributes(x),ax)

    names(weights) <- NULL
    return(list(x=x, sum.of.weights=weights))
  }

  xx <- x
  #if(isdate)
  #  attributes(xx) <- c(attributes(xx),ax)

  if(type=='list')
    list(x=if(length(lev))lev[x]
           else xx, 
         sum.of.weights=weights)
  else {
    names(weights) <- if(length(lev)) lev[x]
                      else xx
    weights
  }
}

##############################
## end functions from Hmisc ##
##############################
